Trustchain -- Trustworthy Decentralised Public Key Infrastructure for Digital Credentials

The sharing of public key information is central to the digital credential security model, but the existing Web PKI with its opaque Certification Authorities and synthetic attestations serves a very different purpose. We propose a new approach to decentralised public key infrastructure, designed for digital identity, in which connections between legal entities that are represented digitally correspond to genuine, pre-existing relationships between recognisable institutions. In this scenario, users can judge for themselves the level of trust they are willing to place in a given chain of attestations. Our proposal includes a novel mechanism for establishing a root of trust in a decentralised setting via independently-verifiable timestamping. We also present a reference implementation built on open networks, protocols and standards. The system has minimal setup costs and is freely available for any community to adopt as a digital public good.Comment: 10 pages, 4 figures, presented at the International Conference on AI and the Digital Economy (CADE 2023), Venice, Italy. Replaces the preprint version, with minor changes & additions based on reviewers' comment

Hard Negative Samples Emphasis Tracker without Anchors

Trackers based on Siamese network have shown tremendous success, because of their balance between accuracy and speed. Nevertheless, with tracking scenarios becoming more and more sophisticated, most existing Siamese-based approaches ignore the addressing of the problem that distinguishes the tracking target from hard negative samples in the tracking phase. The features learned by these networks lack of discrimination, which significantly weakens the robustness of Siamese-based trackers and leads to suboptimal performance. To address this issue, we propose a simple yet efficient hard negative samples emphasis method, which constrains Siamese network to learn features that are aware of hard negative samples and enhance the discrimination of embedding features. Through a distance constraint, we force to shorten the distance between exemplar vector and positive vectors, meanwhile, enlarge the distance between exemplar vector and hard negative vectors. Furthermore, we explore a novel anchor-free tracking framework in a per-pixel prediction fashion, which can significantly reduce the number of hyper-parameters and simplify the tracking process by taking full advantage of the representation of convolutional neural network. Extensive experiments on six standard benchmark datasets demonstrate that the proposed method can perform favorable results against state-of-the-art approaches.Comment: accepted by ACM Mutlimedia Conference, 202

A review of metrology in lithium-ion electrode coating processes

Lithium-ion battery electrode design and manufacture is a multi-faceted process where the link between underlying physical processes and manufacturing outputs is not yet fully understood. This is in part due to the many parameters and variables involved and the lack of complete data sets under different processing conditions. The slurry coating step has significant implications for electrode design and advanced metrology offers opportunities to improve understanding and control at this stage. Here, metrology options for slurry coating are reviewed as well as opportunities for in-line integration, discussing the benefits of combining advanced metrology to provide comprehensive characterisation, improve understanding and feed into predictive design models. There is a comprehensive range of metrology which needs little improvement to provide the relevant quantifiable measures during coating, with one exception of particle sizing, where more precise, in-line measurement would be beneficial. However, there is a lack of studies that bring together the latest advancements in electrode coating metrology which is crucial to understanding the interdependency of myriad processing and product parameters. This review highlights the need for a comprehensive metrological picture whose value would be much greater than the sum of its parts for the next generation of multiphysics and data-driven models

The opto-mechanical design of the GMT-Consortium Large Earth Finder (G-CLEF)

The GMT-Consortium Large Earth Finder (G-CLEF) will be part of the first generation instrumentation suite for the Giant Magellan Telescope (GMT). G-CLEF is a general purpose echelle spectrograph operating in the optical passband with precision radial velocity (PRV) capability. The measurement precision goal of G-CLEF is 10 cm/sec; necessary for the detection of Earth analogues. This goal imposes challenging stability requirements on the optical mounts and spectrograph support structures especially when considering the instrument's operational environment. G-CLEF's accuracy will be influenced by changes in temperature and ambient air pressure, vibration, and micro gravity-vector variations caused by normal telescope motions. For these reasons we have chosen to enclose G-CLEF's spectrograph in a wellinsulated, vibration-isolated vacuum chamber in a gravity invariant location on GMT's azimuth platform. Additional design constraints posed by the GMT telescope include; a limited space envelope, a thermal leakage ceiling, and a maximum weight allowance. Other factors, such as manufacturability, serviceability, available technology, and budget are also significant design drivers. G-CLEF will complete its Critical Design phase in mid-2018. In this paper, we discuss the design of GCLEF's optical mounts and support structures including the choice of a low-CTE carbon-fiber optical bench. We discuss the vacuum chamber and vacuum systems. We discuss the design of G-CLEF's insulated enclosure and thermal control systems which simultaneously maintain the spectrograph at milli-Kelvin level stability and limit thermal leakage into the telescope dome. Also discussed are micro gravity-vector variations caused by normal telescope slewing, their uncorrected influence on image motion, and how they are dealt with in the design. We discuss G-CLEF's front-end assembly and fiber-feed system as well as other interface, integration and servicing challenges presented by the telescope, enclosure, and neighboring instrumentation. This work has been supported by the GMTO Corporation, a non-profit organization operated on behalf of an international consortium of universities and institutions: Arizona State University, Astronomy Australia Ltd, the Australian National University, the Carnegie Institution for Science, Harvard University, the Korea Astronomy and Space Science Institute, the São Paulo Research Foundation, the Smithsonian Institution, the University of Texas at Austin, Texas AM University, the University of Arizona, and the University of Chicago

Roadmap on Li-ion battery manufacturing research

Growth in the Li-ion battery market continues to accelerate, driven primarily by the increasing need for economic energy storage for electric vehicles. Electrode manufacture by slurry casting is the first main step in cell production but much of the manufacturing optimisation is based on trial and error, know-how and individual expertise. Advancing manufacturing science that underpins Li-ion battery electrode production is critical to adding to the electrode manufacturing value chain. Overcoming the current barriers in electrode manufacturing requires advances in materials, manufacturing technology, in-line process metrology and data analytics, and can enable improvements in cell performance, quality, safety and process sustainability. In this roadmap we explore the research opportunities to improve each stage of the electrode manufacturing process, from materials synthesis through to electrode calendering. We highlight the role of new process technology, such as dry processing, and advanced electrode design supported through electrode level, physics-based modelling. Progress in data driven models of electrode manufacturing processes is also considered. We conclude there is a growing need for innovations in process metrology to aid fundamental understanding and to enable feedback control, an opportunity for electrode design to reduce trial and error, and an urgent imperative to improve the sustainability of manufacture

The Thermal Infrared Visual Object Tracking VOT-TIR2015 challenge results

The Thermal Infrared Visual Object Tracking challenge 2015, VOT-TIR2015, aims at comparing short-term single-object visual trackers that work on thermal infrared (TIR) sequences and do not apply pre-learned models of object appearance. VOT-TIR2015 is the first benchmark on short-term tracking in TIR sequences. Results of 24 trackers are presented. For each participating tracker, a short description is provided in the appendix. The VOT-TIR2015 challenge is based on the VOT2013 challenge, but introduces the following novelties: (i) the newly collected LTIR (Link - ping TIR) dataset is used, (ii) the VOT2013 attributes are adapted to TIR data, (iii) the evaluation is performed using insights gained during VOT2013 and VOT2014 and is similar to VOT2015

Rac1 Deletion Causes Thymic Atrophy

The thymic stroma supports T lymphocyte development and consists of an epithelium maintained by thymic epithelial progenitors. The molecular pathways that govern epithelial homeostasis are poorly understood. Here we demonstrate that deletion of Rac1 in Keratin 5/Keratin 14 expressing embryonic and adult thymic epithelial cells leads to loss of the thymic epithelial compartment. Rac1 deletion led to an increase in c-Myc expression and a generalized increase in apoptosis associated with a decrease in thymic epithelial proliferation. Our results suggest Rac1 maintains the epithelial population, and equilibrium between Rac1 and c-Myc may control proliferation, apoptosis and maturation of the thymic epithelial compartment. Understanding thymic epithelial maintenance is a step toward the dual goals of in vitro thymic epithelial cell culture and T cell differentiation, and the clinical repair of thymic damage from graft-versus-host-disease, chemotherapy or irradiation

Early ultrasound surveillance of newly-created haemodialysis arteriovenous fistula

IntroductionWe assess if ultrasound surveillance of newly-created arteriovenous fistulas (AVFs) can predict nonmaturation sufficiently reliably to justify randomized controlled trial (RCT) evaluation of ultrasound-directed salvage intervention.MethodsConsenting adults underwent blinded fortnightly ultrasound scanning of their AVF after creation, with scan characteristics that predicted AVF nonmaturation identified by logistic regression modeling.ResultsOf 333 AVFs created, 65.8% matured by 10 weeks. Serial scanning revealed that maturation occurred rapidly, whereas consistently lower fistula flow rates and venous diameters were observed in those that did not mature. Wrist and elbow AVF nonmaturation could be optimally modeled from week 4 ultrasound parameters alone, but with only moderate positive predictive values (PPVs) (wrist, 60.6% [95% confidence interval, CI: 43.9–77.3]; elbow, 66.7% [48.9–84.4]). Moreover, 40 (70.2%) of the 57 AVFs that thrombosed by week 10 had already failed by the week 4 scan, thus limiting the potential of salvage procedures initiated by that scan’s findings to alter overall maturation rates. Modeling of the early ultrasound characteristics could also predict primary patency failure at 6 months; however, that model performed poorly at predicting assisted primary failure (those AVFs that failed despite a salvage attempt), partly because patency of at-risk AVFs was maintained by successful salvage performed without recourse to the early scan data.ConclusionEarly ultrasound surveillance may predict fistula maturation, but is likely, at best, to result in only very modest improvements in fistula patency. Power calculations suggest that an impractically large number of participants (>1700) would be required for formal RCT evaluation

Roadmap on Li-ion battery manufacturing research

Growth in the Li-ion battery market continues to accelerate, driven by increasing need for economic energy storage in the electric vehicle market. Electrode manufacture is the first main step in production and in an industry dominated by slurry casting, much of the manufacturing process is based on trial and error, know-how and individual expertise. Advancing manufacturing science that underpins Li-ion battery electrode production is critical to adding value to the electrode manufacturing value chain. Overcome the current barriers in the electrode manufacturing requires advances in material innovation, manufacturing technology, in-line process metrology and data analytics to improve cell performance, quality, safety and process sustainability. In this roadmap we present where fundamental research can impact advances in each stage of the electrode manufacturing process from materials synthesis to electrode calendering. We also highlight the role of new process technology such as dry processing and advanced electrode design supported through electrode level, physics-based modelling. To compliment this, the progresses in data driven models of full manufacturing processes is reviewed. For all the processes we describe, there is a growing need process metrology, not only to aid fundamental understanding but also to enable true feedback control of the manufacturing process. It is our hope this roadmap will contribute to this rapidly growing space and provide guidance and inspiration to academia and industry