Deep Learning Features at Scale for Visual Place Recognition

The success of deep learning techniques in the computer vision domain has triggered a range of initial investigations into their utility for visual place recognition, all using generic features from networks that were trained for other types of recognition tasks. In this paper, we train, at large scale, two CNN architectures for the specific place recognition task and employ a multi-scale feature encoding method to generate condition- and viewpoint-invariant features. To enable this training to occur, we have developed a massive Specific PlacEs Dataset (SPED) with hundreds of examples of place appearance change at thousands of different places, as opposed to the semantic place type datasets currently available. This new dataset enables us to set up a training regime that interprets place recognition as a classification problem. We comprehensively evaluate our trained networks on several challenging benchmark place recognition datasets and demonstrate that they achieve an average 10% increase in performance over other place recognition algorithms and pre-trained CNNs. By analyzing the network responses and their differences from pre-trained networks, we provide insights into what a network learns when training for place recognition, and what these results signify for future research in this area.Comment: 8 pages, 10 figures. Accepted by International Conference on Robotics and Automation (ICRA) 2017. This is the submitted version. The final published version may be slightly differen

Acoustic cavitation characterisation in viscous deep eutectic solvents for optimisation of sonoprocessing of technology critical materials

The UK alone produced a total of 1.6 Mt of electronic waste in 2019, containing approximately 380,000 kg of technology critical metals worth $148 M per annum. Within this, printed circuit boards (PCBs) are the largest source of metals from electronic waste, containing up to 30-40 wt.% of technology critical metals. Traditional recycling techniques lack selectivity and have significant environmental and health impact. Ionometallurgy is a promising new technique for recovering metals from electronic waste using deep eutectic solvents (DESs). These solvents offer distinct advantage over traditional techniques, including much lower temperature requirements, avoidance of toxic reagents and reduced water consumption. DESs are cheap, readily available and can be adapted for selectivity. Despite these advantages, DESs are limited by slow dissolution kinetics primarily due to slow mass transport associated with their high viscosities. Power ultrasonics presents a useful solution to these issues. Sonication in DES is hypothesised to increase mass transport, remove passivating surface layers and promote cavitation-mediated effects. However, study into the cavitation activity in solutions other than water are limited. For efficient processing, cavitation generated at the tip of a sonotrode as a function of input power is required. This work is the first comprehensive investigation of cavitation in DESs, for process optimisation to enhance precious metal recycling. Detailed characterisation of the cavitation generated by two sonotrodes in a number of DESs of varying viscosity and water is performed. High-speed imaging (HSI) and acoustic detection from a novel in-house constructed cavitation detector, characterised and validated against a commercially available cavitation sensor (NPL CaviSensorTM), identifies potentially optimal sonication parameters in each liquid. Detailed characterisation of each DES combining synchronised acoustic detection and HSI reveals generation of specific cavitation dynamics and associated cavitation structure, often characterised by a densely packed bulbous cavitation cloud, generating multi-fronted shockwaves. The sonotrode is deployed in DES for the delamination of technology critical metals from waste PCBs. Sonication was observed to delaminate the metals from the PCB at a rate over thirty times faster than in silent conditions. Furthermore, an optimally identified lower power sonication was shown to delaminate a greater quantity of metals from the PCB compared to a higher power sonication, over the same duration. The sonotrode is also deployed to investigate delamination of alternative technology critical resources; lithium-ion batteries and photovoltaics, as well as for rate enhancement of electrodissolution of copper. Further collaborative studies investigate single-bubble dynamics for validation of modelling in the audible frequency range, with interesting potential applications. The results of the studies in this thesis demonstrate the utility and validity of proper cavitation characterisation in solutions intended for sonoprocessing. This characterisation can be performed simply, using bespoke, cheap passive cavitation detectors to gather acoustic measurements at sufficiently fine incremental input powers. Identification of optimal powers of any ultrasonic system for maximum cavitation efficiency is of relevance to many potential processes. In particular, the need for green technologies for electronic waste recycling, could present an ideal problem that can be tackled by ultrasonically enhanced ionometallurgy

A Simulation Study on Multicomponent Lipid Bilayer

Simulation of a multicomponent lipid bilayer having a fixed percentage of cholesterol is done to study phase transition leading to domain formation. The concept of random lattice has been used in simulation to account for the coupling between the internal and translational degrees of freedom of lipid molecules. Considering a canonical ensemble, dissimilar lipid molecules are allowed to exchange their positions in the lattice subject to standard metropolis algorithm. The steps involved in the process effectively takes into account for the movement of sphingolipids and cholesterol molecules helping formation of cholesterol rich domains of saturated lipids as found in natural membranes

Artificially Intelligent Medical Assistant Robot (AIMAR)

Healthcare providers face financial, regulatory, and logistical obstacles in supplying quality care. Applying robotics and artificial intelligence (AI) to healthcare reduces demands on providers, increases accuracy by supplementing medical diagnoses, and improves patient outcomes. Team AIMAR (Artificially Intelligent Medical Assistant Robot) has constructed a modular robotic healthcare AI system, consisting of advanced diagnostic features as supplements to a generic base. The team focused on analyzing images with machine learning to identify skin conditions. The base robot can move around the home or hospital, pick up objects, and interact with patients and doctors. The patient can log in using face authentication so that patient data is secure, and interact verbally and visually through the user interface. New features can easily be added to the base robot's existing integrated features, making AIMAR adaptable for many desired contexts.IPST/LCV Lab, Gemstone Honors Progra

Analysis and Exploitation of Musician Social Networks for Recommendation and Discovery

This paper presents an extensive analysis of a sample of a social network of musicians. The network sample is first analyzed using standard complex network techniques to verify that it has similar properties to other web-derived complex networks. Content-based pairwise dissimilarity values between the musical data associated with the network sample are computed, and the relationship between those content-based distances and distances from network theory explored. Following this exploration, hybrid graphs and distance measures are constructed, and used to examine the community structure of the artist network. Finally, results of these investigations are presented and considered in the light of recommendation and discovery applications with these hybrid measures as their basis

Anomalous versus slowed-down Brownian diffusion in the ligand-binding equilibrium

Measurements of protein motion in living cells and membranes consistently report transient anomalous diffusion (subdiffusion) which converges back to a Brownian motion with reduced diffusion coefficient at long times, after the anomalous diffusion regime. Therefore, slowed-down Brownian motion could be considered the macroscopic limit of transient anomalous diffusion. On the other hand, membranes are also heterogeneous media in which Brownian motion may be locally slowed-down due to variations in lipid composition. Here, we investigate whether both situations lead to a similar behavior for the reversible ligand-binding reaction in 2d. We compare the (long-time) equilibrium properties obtained with transient anomalous diffusion due to obstacle hindrance or power-law distributed residence times (continuous-time random walks) to those obtained with space-dependent slowed-down Brownian motion. Using theoretical arguments and Monte-Carlo simulations, we show that those three scenarios have distinctive effects on the apparent affinity of the reaction. While continuous-time random walks decrease the apparent affinity of the reaction, locally slowed-down Brownian motion and local hinderance by obstacles both improve it. However, only in the case of slowed-down Brownian motion, the affinity is maximal when the slowdown is restricted to a subregion of the available space. Hence, even at long times (equilibrium), these processes are different and exhibit irreconcilable behaviors when the area fraction of reduced mobility changes.Comment: Biophysical Journal (2013

Drazin-Moore-Penrose invertibility in rings

Characterizations are given for elements in an arbitrary ring with involution, having a group inverse and a Moore-Penrose inverse that are equal and the difference between these elements and EP-elements is explained. The results are also generalized to elements for which a power has a Moore-Penrose inverse and a group inverse that are equal. As an application we consider the ring of square matrices of order $m$ over a projective free ring $R$ with involution such that $R^m$ is a module of finite length, providing a new characterization for range-Hermitian matrices over the complexes.Centro de Matemática da Universidade do Minho (CMAT).Fundação para a Ciência e a Tecnologia (FCT) - Programa Operacional "Ciência, Tecnologia, Inovação" (POCTI)

Observation of cavitation dynamics in viscous deep eutectic solvents during power ultrasound sonication

Deep Eutectic Solvents (DESs) are a class of ionic liquid with emerging applications in ionometallurgy. The characteristic high viscosity of DESs, however, limit mass transport and result in slow dissolution kinetics. Through targeted application of high-power ultrasound, ionometallurgical processing time can be significantly accelerated. This acceleration is primarily mediated by the cavitation generated in the liquid surrounding the ultrasound source. In this work, we characterise the development of cavitation structure in three DESs of increasing viscosity, and water, via high-speed imaging and parallel acoustic detection. The intensity of the cavitation is characterised in each liquid as a function of input power of a commercially available ultrasonic horn across more than twenty input powers, by monitoring the bubble collapse shockwaves generated by intense, inertially collapsing bubbles. Through analysis of the acoustic emissions and bubble structure dynamics in each liquid, optimal driving powers are identified where cavitation is most effective. In each of the DESs, driving the ultrasonic horn at lower input powers (25%) was associated with greater cavitation performance than at double the driving power (50%)

Redshift-independent Distances in the NASA/IPAC Extragalactic Database: Methodology, Content, and Use of NED-D

Estimates of galaxy distances based on indicators that are independent of cosmological redshift are fundamental to astrophysics. Researchers use them to establish the extragalactic distance scale, to underpin estimates of the Hubble constant, and to study peculiar velocities induced by gravitational attractions that perturb the motions of galaxies with respect to the "Hubble flow" of universal expansion. In 2006 the NASA/IPAC Extragalactic Database (NED) began making available a comprehensive compilation of redshift-independent extragalactic distance estimates. A decade later, this compendium of distances (NED-D) now contains more than 100,000 individual estimates based on primary and secondary indicators, available for more than 28,000 galaxies, and compiled from over 2000 references in the refereed astronomical literature. This paper describes the methodology, content, and use of NED-D, and addresses challenges to be overcome in compiling such distances. Currently, 75 different distance indicators are in use. We include a figure that facilitates comparison of the indicators with significant numbers of estimates in terms of the minimum, 25th percentile, median, 75th percentile, and maximum distances spanned. Brief descriptions of the indicators, including examples of their use in the database, are given in an appendix

First Molecular Epidemiological Study of Cutaneous Leishmaniasis in Libya

Cutaneous leishmaniasis (CL) is caused by protozoan parasites of the genus Leishmania. The disease is characterized by the formation of chronic skin lesions followed by permanent scars and deformation of the infected area. It is distributed in many tropical and subtropical countries with more than 2 million cases every year. During the past few years CL has emerged as a major public health problem in Libya. So far, diagnosis was based on clinical symptoms and microscopic observation of parasites. Disease outbreaks were not investigated and the causative leishmanial species of CL were not identified so far. Our study indicates the presence of two coexisting species: Leishmania major and Leishmania tropica. These results are crucial in order to provide accurate treatment, precise prognosis and appropriate public health control measures. The recent armed conflict in Libya that ended with the Gadhafi regime collapse on October 2011 has affected all aspects of the life in the country. In this study we discussed multiple risk factors that could be associated with this conflict and present major challenges that should be considered by local and national health authorities for evaluating the CL burden and highlighting priority actions for disease control