Controlling Multistability in a Vibro-Impact Capsule System

This is the final version of the article. Available from Springer Verlag via the DOI in this record.This work concerns the control of multistability in a vibro-impact capsule system driven by a harmonic excitation. The capsule is able to move forward and backward in a rectilinear direction, and the main objective of this work is to control such motion in the presence of multiple coexisting periodic solutions. A position feedback controller is employed in this study, and our numerical investigation demonstrates that the proposed control method gives rise to a dynamical scenario with two coexisting solutions, corresponding to forward and backward progression. Therefore, the motion direction of the system can be controlled by suitably perturbing its initial conditions, without altering the system parameters. To study the robustness of this control method, we apply numerical continuation methods in order to identify a region in the parameter space in which the proposed controller can be applied. For this purpose, we employ the MATLAB-based numerical platform COCO, which supports the continuation and bifurcation detection of periodic orbits of non-smooth dynamical systems.The second author has been supported by a Georg Forster Research Fellowship granted by the Alexander von Humboldt Foundation, Germany. The authors would like to thank Dr. Haibo Jiang for stimulating discussions and comments on this work

Controlling multistability in a vibro-impact capsule system

This is the final version of the article. Available from Springer Verlag via the DOI in this record.This work concerns the control of multistability in a vibro-impact capsule system driven by a harmonic excitation. The capsule is able to move forward and backward in a rectilinear direction, and the main objective of this work is to control such motion in the presence of multiple coexisting periodic solutions. A position feedback controller is employed in this study, and our numerical investigation demonstrates that the proposed control method gives rise to a dynamical scenario with two coexisting solutions, corresponding to forward and backward progression. Therefore, the motion direction of the system can be controlled by suitably perturbing its initial conditions, without altering the system parameters. To study the robustness of this control method, we apply numerical continuation methods in order to identify a region in the parameter space in which the proposed controller can be applied. For this purpose, we employ the MATLAB-based numerical platform COCO, which supports the continuation and bifurcation detection of periodic orbits of non-smooth dynamical systems.The second author has been supported by a Georg Forster Research Fellowship granted by the Alexander von Humboldt Foundation, Germany. The authors would like to thank Dr. Haibo Jiang for stimulating discussions and comments on this work

TFA inference: Using mathematical modeling of gene expression data to infer the activity of transcription factors

Transcription factors (TFs) are a set of proteins that play a key role in the information processing system that enables a cell to respond to changes in internal and external state. By binding near a gene in a cell’s DNA, a TF can influence that gene’s expression level, triggering the appropriate increase or decrease in production levels of proteins that are needed to handle stressors like a change in nutrient availability or damage to the cell’s internal structures. Transcription factor activity (TFA) is a measure of how much effect a TF has on its target genes in a given sample of cells. TFA depends on several factors including expression of the gene that encodes the TF, the TF’s access to genes, and how much of the TF protein has the modifications needed to activate it. Because there are so many molecular factors influencing TF activity, there is no one assay that can measure TFA directly.In this dissertation, we build on previous work in TFA inference that uses the measurable output of cell signaling pathways – gene expression levels – to infer TFA values and to utilize these inferred values to better understand the roles of individual TFs within gene regulatory systems. First, we applied TFA inference to microarray data on the well-studied Saccharomyces cerevisiae (baker’s yeast) in order to define systematic, objective accuracy metrics. With these metrics, we explore the robustness of TFA inference to changes in the studied organism, the type of data input, and the optimization approach. Finally, we optimize the TFA inference algorithm to study RNA-seq data from a pathogenic yeast, Cryptococcus neoformans, to analyze the signaling pathway involved in its capsule formation response to environmental stress, a major factor of its virulence in humans

Remote sensing of opium poppy cultivation in Afghanistan

This work investigates differences in the survey methodologies of the monitoring programmes of the United Nations Office on Drugs and Crime (UNODC) and the US Government that lead to discrepancies in quantitative information about poppy cultivation. The aim of the research is to improve annual estimates of opium production. Scientific trials conducted for the UK Government (2006–2009) revealed differences between the two surveys that could account for the inconsistency in results. These related to the image interpretation of poppy from very high resolution satellite imagery, the mapping of the total area of agriculture and stratification using full coverage medium resolution imagery. MODIS time-series profiles of Normalised Difference Vegetation Index (NDVI), used to monitor Afghanistan’s agricultural system, revealed significant variation in the agriculture area between years caused by land management practices and expansion into new areas. Image interpretation of crops was investigated as a source of bias within the sample using increasing levels of generalisation in sample interpretations. Automatic segmentation and object-based classification were tested as methods to improve consistency. Generalisation was found to bias final estimates of poppy up to 14%. Segments were consistent with manual field delineations but object-based classification caused a systematic labelling error. The findings show differences in survey estimates based on interpretation keys and the resolution of imagery, which is compounded in areas of marginal agriculture or years with poor crop establishment. Stratified and unstratified poppy cultivation estimates were made using buffered and unbuffered agricultural masks at resolutions of 20, 30 and 60 m, resampled from SPOT-5 10 m data. The number of strata (1, 4, 8, 13, 23, 40) and sample fraction (0.2 to 2%) used in the estimate were also investigated. Decreasing the resolution of the imagery and buffering increased unstratified estimates. Stratified estimates were more robust to changes in sample size and distribution. The mapping of the agricultural area explained differences in cultivation figures of the opium monitoring programmes in Afghanistan. Supporting methods for yield estimation for opium poppy were investigated at field sites in the UK in 2004, 2005 and 2010. Good empirical relationships were found between NDVI and the yield indicators of mature capsule volume and dry capsule yield. The results suggested a generalised relationship across all sampled fields and years (R2 >0.70) during the 3–4 week period including poppy flowering. The application of this approach in Afghanistan was investigated using VHR satellite imagery and yield data from the UNODC’s annual survey. Initial results indicated the potential of improved yield estimates using a smaller and targeted collection of ground observations as an alternative to random sampling. The recommendations for poppy cultivation surveys are: the use of image-based stratification for improved precision and reducing differences in the agricultural mask, and use of automatic segmentation for improved consistency in field delineation of poppy crops. The findings have wider implications for improved confidence in statistical estimates from remote sensing methodologies

Very High Resolution (VHR) Satellite Imagery: Processing and Applications

Recently, growing interest in the use of remote sensing imagery has appeared to provide synoptic maps of water quality parameters in coastal and inner water ecosystems;, monitoring of complex land ecosystems for biodiversity conservation; precision agriculture for the management of soils, crops, and pests; urban planning; disaster monitoring, etc. However, for these maps to achieve their full potential, it is important to engage in periodic monitoring and analysis of multi-temporal changes. In this context, very high resolution (VHR) satellite-based optical, infrared, and radar imaging instruments provide reliable information to implement spatially-based conservation actions. Moreover, they enable observations of parameters of our environment at greater broader spatial and finer temporal scales than those allowed through field observation alone. In this sense, recent very high resolution satellite technologies and image processing algorithms present the opportunity to develop quantitative techniques that have the potential to improve upon traditional techniques in terms of cost, mapping fidelity, and objectivity. Typical applications include multi-temporal classification, recognition and tracking of specific patterns, multisensor data fusion, analysis of land/marine ecosystem processes and environment monitoring, etc. This book aims to collect new developments, methodologies, and applications of very high resolution satellite data for remote sensing. The works selected provide to the research community the most recent advances on all aspects of VHR satellite remote sensing

Verifiably encrypted cascade-instantiable blank signatures to secure progressive decision management

National Research Foundation (NRF) Singapore under NC

Modern Machine Learning for LHC Physicists

Modern machine learning is transforming particle physics, faster than we can follow, and bullying its way into our numerical tool box. For young researchers it is crucial to stay on top of this development, which means applying cutting-edge methods and tools to the full range of LHC physics problems. These lecture notes are meant to lead students with basic knowledge of particle physics and significant enthusiasm for machine learning to relevant applications as fast as possible. They start with an LHC-specific motivation and a non-standard introduction to neural networks and then cover classification, unsupervised classification, generative networks, and inverse problems. Two themes defining much of the discussion are well-defined loss functions reflecting the problem at hand and uncertainty-aware networks. As part of the applications, the notes include some aspects of theoretical LHC physics. All examples are chosen from particle physics publications of the last few years. Given that these notes will be outdated already at the time of submission, the week of ML4Jets 2022, they will be updated frequently.Comment: First version, we very much appreciate feedbac

Advances in Object and Activity Detection in Remote Sensing Imagery

The recent revolution in deep learning has enabled considerable development in the fields of object and activity detection. Visual object detection tries to find objects of target classes with precise localisation in an image and assign each object instance a corresponding class label. At the same time, activity recognition aims to determine the actions or activities of an agent or group of agents based on sensor or video observation data. It is a very important and challenging problem to detect, identify, track, and understand the behaviour of objects through images and videos taken by various cameras. Together, objects and their activity recognition in imaging data captured by remote sensing platforms is a highly dynamic and challenging research topic. During the last decade, there has been significant growth in the number of publications in the field of object and activity recognition. In particular, many researchers have proposed application domains to identify objects and their specific behaviours from air and spaceborne imagery. This Special Issue includes papers that explore novel and challenging topics for object and activity detection in remote sensing images and videos acquired by diverse platforms

Privacy-preserving decentralised collaborative applications

Cloud-based applications are problematic from a privacy perspective because they typically have access to large amounts of user data and metadata. This centralisation of user data creates an attractive target for actors such as criminals, suppressive governments, and companies selling the data. At the same time, the popularity of mobile and web applications has led to a growing amount of sensitive data being stored in the cloud. This dissertation focuses on collaborative applications, such as Google Docs and Microsoft Office Online, where users currently rely on cloud-based solutions. It explores decentralised alternatives that allow the use of end-to-end encryption and anonymous communication systems to improve both information privacy and communication privacy. One approach for a collaborative application to synchronise data in a privacy-preserving way is to use Tor hidden services, providing end-to-end encrypted communication, while also hiding collaborators’ identity. However, running Tor comes at a cost. We explore the costs of running a hidden service on a smartphone. Smartphones are nowadays the most frequently used computing devices, but they are also relatively resource-constrained. We build an empirical model of monthly cellular data traffic, and estimate a median 198 MiB for a typical user. We further estimate that the network activity would cost at least 9.6% of daily battery capacity on a Nexus One using 3G Internet. We explore four optimisations that, in combination, reduce the estimated median data cost to 61 MiB. We also consider the security and privacy properties of decentralised collaborative applications, and explore a challenge that is introduced by a decentralised design – the lack of a trusted server guaranteeing consistency between collaborators. We present a novel snapshot protocol that ensures consistency, whilst allowing the past edit history to be hidden from new collaborators, and without relying on a consensus mechanism. Lastly, we evaluate the overhead of the snapshot protocol by replaying editing histories from 270 Wikipedia articles, and demonstrate how its correctness and security properties are achieved. Assuming the number of collaborators remains small, the protocol is scalable in terms of CPU, memory, and network usage. It substantially reduces the amount of data transferred to a new collaborator compared to a basic protocol that transmits the full history. The computational cost is in the order of milliseconds per operation, indicating the protocol is suitable for applications where the rate of edits is relatively low.Funding was provided by Microsoft Research, The Boeing Company, and the Computer Laboratory