cometadata/datacite-rtg-text-other-reclassification-records huggingface dataset:
Discovery of synthetic lethal interactions from large-scale pan-cancer perturbation screens
Creators: name: Srivatsa, Sumana
name: Montazeri, Hesam
name: Bianco, Gaia
name: Coto-Llerena, Mairene
name: Ng, Charlotte K.Y.
name: Piscuoglio, Salvatore
name: Beerenwinkel, Niko
Publisher: name: ETH Zurich
Year: 2019
Subjects: SLIdR
Synthetic lethality (SL)
Perturbation screen
Driver genes
SL partner
Causal inference
AXIN1-URI1

description: Despite the progress in precision oncology, development of cancer therapies is limited by the dearth of suitable drug targets1. Novel candidate drug targets can be identified based on the concept of synthetic lethality (SL), which refers to pairs of genes for which an aberration in either gene alone is non-lethal, but co-occurrence of the aberrations is lethal to the cell. We developed SLIdR (Synthetic Lethal Identification in R), a statistical framework for identifying SL pairs from large-scale perturbation screens. SLIdR successfully predicts SL pairs even with small sample sizes while minimizing the number of false positive targets. We applied SLIdR to Project DRIVE data2 and found both established and novel pan-cancer and cancer type-specific SL pairs. We identified and experimentally validated a novel SL interaction between AXIN1 and URI1 in hepatocellular carcinoma, thus corroborating the potential of SLIdR to identify new SL-based drug targets.
description: bioRxiv
The Physiological Landscape and Specificity of Antibody Repertoires
Creators: name: Csepregi, Lucia
name: Hoehn, Kenneth B.
name: Neumeier, Daniel
name: Taft, Joseph M.
name: Friedensohn, Simon
name: Weber, Cédric
name: Kummer, Arkadij
name: Sesterhenn, Fabian
name: Correia, Bruno E.
name: Reddy, Sai T.
Publisher: name: ETH Zurich
Year: 2021

description: Diverse antibody repertoires spanning multiple lymphoid organs (e.g., bone marrow, spleen, lymph nodes) form the foundation of protective humoral immunity. Changes in their composition across lymphoid organs are a consequence of B-cell selection and migration events leading to a highly dynamic and unique physiological landscape of antibody repertoires upon antigenic challenge (e.g., vaccination). However, to what extent B cells encoding identical or similar antibody sequences (clones) are distributed across multiple lymphoid organs and how this is shaped by the strength of a humoral response, remains largely unexplored. Here, we performed an in-depth systems analysis of antibody repertoires across multiple distinct lymphoid organs of immunized mice, and discovered that organ-specific antibody repertoire features (e.g., germline V-gene usage and clonal expansion profiles) equilibrated upon a strong humoral response (multiple immunizations and high serum titers). This resulted in a surprisingly high degree of repertoire consolidation, characterized by highly connected and overlapping B-cell clones across multiple lymphoid organs. Finally, we revealed distinct physiological axes indicating clonal migrations and showed that antibody repertoire consolidation directly correlated with antigen-specificity. Our study uncovered how a strong humoral response resulted in a more uniform but redundant physiological landscape of antibody repertoires, indicating that increases in antibody serum titers were a result of synergistic contributions from antigen-specific B-cell clones distributed across multiple lymphoid organs. Our findings provide valuable insights for the assessment and design of vaccine strategies.
description: bioRxiv
Online Training of Spiking Recurrent Neural Networks with Phase-Change Memory Synapses
Creators: name: Demirağ, Yiğit
name: Frenkel, Charlotte
name: Payvand, Melika
name: Indiveri, Giacomo
Publisher: name: ETH Zurich
Year: 2021

description: Spiking recurrent neural networks (RNNs) are a promising tool for solving a wide variety of complex cognitive and motor tasks, due to their rich temporal dynamics and sparse processing. However training spiking RNNs on dedicated neuromorphic hardware is still an open challenge. This is due mainly to the lack of local, hardware-friendly learning mechanisms that can solve the temporal credit assignment problem and ensure stable network dynamics, even when the weight resolution is limited. These challenges are further accentuated, if one resorts to using memristive devices for in-memory computing to resolve the von-Neumann bottleneck problem, at the expense of a substantial increase in variability in both the computation and the working memory of the spiking RNNs. To address these challenges and enable online learning in memristive neuromorphic RNNs, we present a simulation framework of differential-architecture crossbar arrays based on an accurate and comprehensive Phase-Change Memory (PCM) device model. We train a spiking RNN whose weights are emulated in the presented simulation framework, using a recently proposed e-prop learning rule. Although e-prop locally approximates the ideal synaptic updates, it is difficult to implement the updates on the memristive substrate due to substantial PCM non-idealities. We compare several widely adapted weight update schemes that primarily aim to cope with these device non-idealities and demonstrate that accumulating gradients can enable online and efficient training of spiking RNN on memristive substrates.
description: arXiv
Modeling Electrical Resistance Drift with Ultrafast Saturation of OTS Selectors
Creators: name: Demirağ, Yiğit
name: Özbay, Ekmel
name: Leblebici, Yusuf
Publisher: name: ETH Zurich
Year: 2021
Subjects: OTS selectors
Drift problem
Device modeling
Neuromorphic hardware

description: Crossbar array architecture is an essential design element for densely connected Non-Volatile Memory(NVM) applications. To overcome intrinsic sneak current problem of crossbar arrays, each memory unit is serially attached to a selector unit with highly nonlinear current-voltage (I-V) characteristics. Recently, Ovonic Threshold Switching (OTS) materials are preferred as selectors due to their fabrication compatibility with PRAM, MRAM or ReRAM technologies; however, OTS selectors suffer from the temporal drift of its threshold voltage. First, based on Poole-Frenkel conduction, we present time and temperature dependent model that predicts temporally evolving I-V characteristics,including threshold voltage of OTS selectors. Second, we report an ultrafast saturation (103 seconds) of the drift and extend the model to predict the time of drift saturation. Our model shows excellent agreement with OTS devices fabricated with 8 nm technology node at 25C and 85C ambient temperatures. The proposed model plays a significant role in understanding OTS device internals and the development of reliable threshold voltage jump table.
description: arXiv
Entretien de Raouf Abdallah al-Sharifayn auprès de Saleh Daradka à propos de la première guerre mondiale et de l'Empire ottoman en Jordanie
Creators: name: Daradka, Saleh
name: Raouf Abdallah Al-Sharifayn (Enquêteur)
Publisher: name: NAKALA - https://nakala.fr (Huma-Num - CNRS)
publisherIdentifier: https://ror.org/04ces3204
publisherIdentifierScheme: ROR
Year: 2020

description: Source: http://www.calames.abes.fr/pub/ms/FileId-3139
description: Coverage: Irbid, Jordanie
Ожирение как фактор риска хронических неинфекционных заболеваний.РКЖ 2016;(6)73-79.pdf
Creators: name: Doe, Jonn
Publisher: name: figshare
Year: 2016
Subjects: subject: Cardiology
subject: 110201 Cardiology (incl. Cardiovascular Diseases)
subject: FOS: Clinical medicine
subject: 110306 Endocrinology

description: Ожирение как фактор риска хронических неинфекционных заболеваний.РКЖ 2016;(6)73-79.pdf
CWcollab: A Context-Aware Web-Based Collaborative Multimedia System
Creators: name: Tang, Chunxu
name: Wang, Beinan
name: Chen, C. Y. Roger
name: Wu, Huijun
Publisher: name: arXiv
Year: 2022
Subjects: subject: Multimedia (cs.MM)
subject: FOS: Computer and information sciences

description: Remote collaboration tools for conferencing and presentation are gaining significant popularity during the COVID-19 pandemic period. Most prior work has issues, such as a) limited support for media types, b) lack of interactivity, for example, an efficient replay mechanism, c) large bandwidth consumption for screen sharing tools. In this paper, we propose a general-purpose multimedia collaboration platform-CWcollab. It supports collaboration on general multimedia by using simple messages to represent media controls with an object-prioritized synchronization approach. Thus, CWcollab can not only support fine-grained accurate collaboration, but also rich functionalities such as replay of these collaboration events. The evaluation shows hundreds of kilobytes can be enough to store the events in a collaboration session for accurate replays, compared with hundreds of megabytes of Google Hangouts.
A Canonical Transform for Strengthening the Local Lp-Type Universal Approximation Property
Creators: name: Kratsios, Anastasis
name: Zamanlooy, Behnoosh
Publisher: name: ETH Zurich
Year: 2021
Subjects: Universal Approximation
UAP Upgrading
Strict Topologies
Colimits of Topological Spaces

description: Most Lp-type universal approximation theorems guarantee that a given machine learning model class ℱ⊆C(ℝd,ℝD) is dense in Lpμ(ℝd,ℝD) for any suitable finite Borel measure μ on ℝd. Unfortunately, this means that the model's approximation quality can rapidly degenerate outside some compact subset of ℝd, as any such measure is largely concentrated on some bounded subset of ℝd. This paper proposes a generic solution to this approximation theoretic problem by introducing a canonical transformation which "upgrades ℱ's approximation property" in the following sense. The transformed model class, denoted by ℱ-tope, is shown to be dense in Lpμ,strict(ℝd,ℝD) which is a topological space whose elements are locally p-integrable functions and whose topology is much finer than usual norm topology on Lpμ(ℝd,ℝD); here μ is any suitable σ-finite Borel measure μ on ℝd. Next, we show that if ℱ is any family of analytic functions then there is always a strict "gap" between ℱ-tope's expressibility and that of ℱ, since we find that ℱ can never dense in Lpμ,strict(ℝd,ℝD). In the general case, where ℱ may contain non-analytic functions, we provide an abstract form of these results guaranteeing that there always exists some function space in which ℱ-tope is dense but ℱ is not, while, the converse is never possible. Applications to feedforward networks, convolutional neural networks, and polynomial bases are explored.
description: arXiv
Evolutionary PTEN gene divergence underpins the remodeling of plant vacuolar compartments
Creators: name: Gujas, Bojan
name: Champeyroux, Chloe
name: Hunkeler, Anna
name: Robinson, Emilija
name: Blanco-Touriñán, Noel
name: Dias Cruz, Tiago Miguel
name: Van Durme, Matthias
name: Nowack, Moritz K.
name: Rodriguez-Villalon, Antia
Publisher: name: ETH Zurich
Year: 2022
Subjects: Vacuole division
Cleavage furrow
Xylem
Cell differentiation
PTEN

description: Membrane fusion and fission are fundamental processes in sustaining cellular compartmentalization. Fission of a lipid bilayer requires a furrow formation that brings membranes in close proximity prior to a contiguous membrane cleavage. Although plant ancestors abandoned cleavage furrow-mediated cytokinesis more than 500 million years ago, here we show that plants still employ this mechanical principle to divide embryonic vacuoles. The evolutionary divergence in PHOSPHATASE AND TENSIN HOMOLOG DELETED ON CHROMOSOME TEN (PTEN) enzymes was required to coordinate this process, as Arabidopsis loss-of-function pten2a pten2b mutants contain hyper compartmentalized embryonic vacuoles. In contrast, PTEN2 overexpression hinders lytic and secretion cellular pathways downstream of TGN in xylem cells. These processes are critical for the formation of secondary cell walls in xylem cells and depend on a poorly characterized and evolutionarily novel N-terminal domain in PTEN2s. The PTEN2 subfamily appeared with the emergence of the Phragmoplastophyta clade, when vacuolar compartments enlarged and cleavage furrow-mediated cytokinesis became extinct. Together, our work suggests that the evolutionary innovation of the PTEN family is conserved across terrestrial plants and central to vacuolar remodelling.
description: bioRxiv
Methodik zur systematischen Analyse der Prozessgestaltung einer Mensch-Roboter-Interaktion in der Montage
Creators: name: Tyroller, Quirin
Publisher: name: Karlsruhe
Year: 2020
Subjects: Mensch Roboter Interaktion
Mensch Roboter Schnittstelle
Montage

description: Die steigenden Anforderungen, die durch volatile Märkte, den Fachkräftemangel sowie wechselnde Regularien an das Produktionssystem im Automobilbau gestellt werden, erfordern eine stetige Weiterentwicklung. Die Integration einer ﬂexiblen Mensch-Roboter-Interaktion in die Montage bietet eine Möglichkeit den Herausforderungen zu begegnen und eröffnet Potentiale für neuartige Produktionskonzepte. Für die Realisierung einer solchen kollaborativen Roboterapplikation ist sowohl die Kenntnis der Bedarfe, als auch die der speziﬁschen Anforderungen des Anwendungsumfelds erforderlich. Anhand der Vorstellung des Stands der Forschung werden diesbezüglich Fragestellungen ermittelt, die vor dem Hintergrund der Montage in der Automobilindustrie über eine zweistuﬁge Potentialanalyse konkretisiert werden. Darin rückt neben der Gestaltung einer Schnittstelle insbesondere die Interaktion der Partner in den Fokus. Das primäre Ziel besteht darin, die Auswirkungen der Interaktion des Menschen mit dem Roboter auf den Prozess mittels einer geeigneten Methode abbilden zu können und dafür ein passendes Schnittstellenkonzept bereitzustellen. Auf Basis dessen werden Schnittstellenkonzepte sowie eine Methodik zur Prozessanalyse entworfen, deren Eignung im Rahmen mehrerer experimenteller Untersuchungen evaluiert wird. Der erste Experimentalblock widmet sich der Eignungsprüfung der Schnittstellenkonzepte, um eine negative Beeinﬂussung der Interaktion vor dem Einsatz in einem kollaborativen Anwendungsfall auszuschließen und erste Implikationen zu erfassen. Der zweite Block dient der Validierung der entwickelten Methodik sowie der Ermittlung des Effekts von Gestaltung und Nutzung der Schnittstelle auf den Prozessablauf. Dies erfolgt anhand einer adaptierten Montageapplikation aus einem Automobilwerk. Die Ergebnisse zeigen auf, dass ein kollaborativer Prozess anhand des aufgestellten Modells ganzheitlich erfassbar ist und die resultierende Aussage sowohl eine efﬁziente Prozessaufteilung, als auch die subjektive Wahrnehmung des menschlichen Prozessteilnehmers widerspiegelt. Im Anschluss an die experimentellen Untersuchungen werden Implikationen für die Gestaltung des Montageprozesses und die Eignung von Schnittstellen diskutiert. Neben den identiﬁzierten Grenzen werden darin Potentiale zur Weiterentwicklung sowie eine Empfehlung zur Gestaltung aufgezeigt.
Echidna: integrated simulations of single-cell immune receptor repertoires and transcriptomes
Creators: name: Han, Jiami
name: Kuhn, Raphael
name: Papadopoulou, Chrysa
name: Agrafiotis, Andreas
name: Kreiner, Victor
name: Shlesinger, Danielle
name: Dizerens, Raphael
name: Hong, Kai-Lin
name: Weber, Cédric
name: Greiff, Victor
name: Oxenius, Annette
name: Reddy, Sai T.
name: Yermanos, Alexander
Publisher: name: ETH Zurich
Year: 2021

description: Single-cell sequencing now enables the recovery of full-length immune repertoires [B cell receptor (BCR) and T cell receptor (TCR) repertoires], in addition to gene expression information. The feature-rich datasets produced from such experiments require extensive and diverse computational analyses, each of which can significantly influence the downstream immunological interpretations, such as clonal selection and expansion. Simulations produce validated standard datasets, where the underlying generative model can be precisely defined and furthermore perturbed to investigate specific questions of interest. Currently, there is no tool that can be used to simulate a comprehensive ground truth single-cell dataset that incorporates both immune receptor repertoires and gene expression. Therefore, we developed Echidna, an R package that simulates immune receptors and transcriptomes at single-cell resolution. Our simulation tool generates annotated single-cell sequencing data with user-tunable parameters controlling a wide range of features such as clonal expansion, germline gene usage, somatic hypermutation, and transcriptional phenotypes. Echidna can additionally simulate time-resolved B cell evolution, producing mutational networks with complex selection histories incorporating class-switching and B cell subtype information. Finally, we demonstrate the benchmarking potential of Echidna by simulating clonal lineages and comparing the known simulated networks with those inferred from only the BCR sequences as input. Together, Echidna provides a framework that can incorporate experimental data to simulate single-cell immune repertoires to aid software development and bioinformatic benchmarking of clonotyping, phylogenetics, transcriptomics and machine learning strategies.
description: bioRxiv
Pervasive eHealth services a security and privacy risk awareness survey
Creators: name: Bellekens, Xavier
name: Seeam, Amar
name: Hamilton, Andrew W
name: Preetila Seeam
name: Nieradzinska, Kamila
Publisher: name: figshare
Year: 2016
Subjects: subject: 170103 Educational Psychology
subject: FOS: Psychology
subject: Applied Psychology
subject: 80303 Computer System Security
subject: FOS: Computer and information sciences
subject: Survey Results

description: —The human factor is often recognised as a major aspect of cyber-security research. Risk and situational perception are identified as key factors in the decision making process, often playing a lead role in the adoption of security mechanisms. However, risk awareness and perception have been poorly investigated in the field of eHealth wearables. Whilst end-users often have limited understanding of privacy and security of wearables, assessing the perceived risks and consequences will help shape the usability of future security mechanisms. This paper present a survey of the the risks and situational awareness in eHealth services. An analysis of the lack of security and privacy measures in connected health devices is described with recommendations to circumvent critical situations.
High-throughput identification of RNA localization elements reveals a regulatory role for A/G rich sequences
Creators: name: Arora, Ankita
name: Gutierrez, Roberto Castro
name: Eletto, Davide
name: Becker, Raquel
name: Brown, Maya
name: Moor, Andreas
name: Russ, Holger A.
name: Taliaferro, J. Matthew
Publisher: name: ETH Zurich
Year: 2021

description: Hundreds of RNAs are enriched in the projections of neuronal cells. For the vast majority of them, though, the sequence elements within them that regulate their localization are unknown. To identify RNA elements capable of directing transcripts to neurites, we designed and deployed a massively parallel reporter assay that tested the localization regulatory ability of thousands of sequence fragments drawn from endogenous mouse 3′ UTRs. We identified peaks of regulatory activity within several 3′ UTRs and found that sequences derived from these peaks were both necessary and sufficient for RNA localization to neurites in mouse cells as well as active in human neurons. The active localization elements were enriched in adenosine and guanosine residues and were tens to hundreds of nucleotides long as shortening of the elements led to significantly reduced activity. Using RNA affinity and mass spectrometry, we found that the RNA-binding protein Unk was associated with the active localization elements. Depletion of Unk in cells reduced the neurite-enrichment of the elements, indicating a functional requirement for Unk in their trafficking. These results provide a framework for the unbiased, high-throughput identification of RNA elements and mechanisms that govern transcript localization.
description: bioRxiv
Emulating Earth System Model land temperature fields with MESMER
Creators: name: Beusch, Lea
name: Gudmundsson, Lukas
name: Seneviratne, Sonia I.
Publisher: name: ETH Zurich
Year: 2021

description: Earth System Models (ESMs) are invaluable tools to study the climate system’s response to greenhouse gas emissions. But their projections are affected by three major sources of uncertainty: (i) internal variability, i.e., natural climate variability, (ii) ESM structural uncertainty, i.e., uncertainty in the response of the climate system to given greenhouse gas concentrations, and (iii) emission scenario uncertainty, i.e., which emission pathway the world chooses. The large computational cost of running full ESMs limits the exploration of this uncertainty phase space since it is only feasible to create a limited number of ESM runs. However, climate change impact and integrated assessment models, which require ESM projections as their input, could profit from a more complete sampling of the climate change uncertainty phase space. In this contribution, we present MESMER (Beusch et al., 2020), a Modular ESM Emulator with spatially Resolved output, which allows for a computationally efficient exploration of the uncertainty space of yearly temperatures. MESMER approximates ESM land temperature fields at a negligible computational cost by expressing grid-point-level temperatures as a function of global mean temperature and an overlaid spatio-temporally correlated variability term. Within MESMER all three major sources of uncertainty can be accounted for. Stochastic simulation of natural climate variability allows to account for internal variability. ESM structural uncertainty can be addressed by calibrating MESMER on different ESMs from the Coupled Model Intercomparison Project (CMIP) archives. Finally, emission scenario uncertainty can be accounted for by ingesting forced global mean temperature trajectories from global climate model emulators, such as MAGICC or FaIR. MESMER is a flexible statistical tool which is under active development and in the process of becoming an open-source software.
description: EGUsphere
Bacterial chemotaxis to saccharides is governed by a trade-off between sensing and uptake
Creators: name: Norris, Noele
name: Alcolombri, Uria
name: Keegstra, Johannes
name: Yawata, Yutaka
name: Menolascina, Filippo
name: Frazzoli, Emilio
name: Levine, Naomi M.
name: Fernandez, Vicente I.
name: Stocker, Roman
Publisher: name: ETH Zurich
Year: 2021
Subjects: Bacterial chemotaxis
ABC transport
Porin-limited transport
Molecular-level model
Population-level motility assay

description: To swim up gradients of nutrients, E. coli senses nutrient concentrations within its periplasm. For small nutrient molecules, periplasmic concentrations typically match extracellular concentrations. However, this is not necessarily the case for saccharides, such as maltose, which is transported into the periplasm via a specific porin. Previous observations have shown that under various conditions E. coli limits maltoporin abundance so that, for extracellular micromolar concentrations of maltose, there are predicted to be only nanomolar concentrations of free maltose in the periplasm. Thus, in the micromolar regime, the total uptake of maltose from the external environment into the cytoplasm is limited not by the abundance of cytoplasmic transport proteins but by the abundance of maltoporins. Here we present results from experiments and modeling showing that this porin-limited transport enables E. coli to sense micromolar gradients of maltose despite having a high-affinity ABC transport system that is saturated at these micromolar levels. We used microfluidic assays to study chemotaxis of E. coli in various gradients of maltose and methyl-aspartate and leveraged our experimental observations to develop a mechanistic transport-and-sensing chemotaxis model. Incorporating this model into agent-based simulations, we discover a trade-off between uptake and sensing: although high-affinity transport enables higher uptake rates at low nutrient concentrations, it severely limits dynamic sensing range. We thus propose that E. coli may limit periplasmic uptake to increase its chemotactic sensitivity, enabling it to use maltose as an environmental cue.
description: bioRxiv
Epidemiology and forecasting of influenza and COVID-19
Creators: name: Wang, Haowei
Publisher: name: Imperial College London
Year: 2023

description: Respiratory infectious diseases (RID) have significant health and social impacts globally, and the speed of transmission and the scale of outbreaks pose significant challenges to public health systems and social stability. As the two major respiratory infectious diseases, influenza and SARS-CoV-2 are both highly contagious and clinically complex. Influenza causes large-scale outbreaks every year, posing a serious threat, especially to vulnerable populations. SARS-CoV-2 has spread rapidly across the world since its emergence in late 2019, leading to the COVID-19 pandemic and triggering public health crises worldwide. Therefore, this PhD thesis focuses on a number of important aspects of influenza and SARS-CoV-2, aiming to gain an in-depth understanding of their transmission status in the community, and prevalence in different subgroups of populations, and to construct reliable forecasting frameworks to support pandemic control and public health decision-making. The thesis systematically analysed the transmission dynamics of SARS-CoV-2 in England through community surveillance, revealing its epidemiological trends and the prevalence of SARS-CoV-2 infection in different populations with respect to their social, economic and living characteristics. The results of the risk factor analysis demonstrated health inequality existed among ethnic groups, household size, and occupational activity, providing a scientific basis for targeted prevention and control and resource allocation. Furthermore, an efficient forecasting framework based on advanced data analysis and machine learning techniques was developed, which can accurately predict the trends of influenza and SARS-CoV-2 transmission, potentially offering a more practical tool for decision-making by the government and health departments than other currently available frameworks. These research results are expected to provide useful references for global epidemic prevention and control, as well as valuable experience for in-depth research and practice in the fields of public health and data science.
Polarimetric Synthetic Aperture Radar: Principles and Application
Creators: name: Hajnsek, Irena
name: Desnos, Yves-Louis
Publisher: name: ETH Zurich
Year: 2021
Subjects: Earth remote sensing
PolSARpro
Radar polarimetry
Radar polarimetry toolbox
Synthesis aperture radar
Open access
Remote sensing/photogrammetry
Urban geography and urbanism

description: This open access book focuses on the practical application of electromagnetic polarimetry principles in Earth remote sensing with an educational purpose. In the last decade, the operations from fully polarimetric synthetic aperture radar such as  the Japanese ALOS/PalSAR, the Canadian Radarsat-2 and the German TerraSAR-X  and their easy data access for scientific use have developed further the research and data applications at L,C and X band. As a consequence, the wider distribution of polarimetric data sets across the remote sensing community boosted activity and development in polarimetric SAR applications, also in view of future missions. Numerous experiments with real data from spaceborne platforms are shown, with the aim of giving an up-to-date and complete treatment of the unique benefits of fully polarimetric synthetic aperture radar data in five different domains: forest, agriculture, cryosphere, urban and oceans.
description: ISBN:978-3-030-56502-2
description: ISBN:978-3-030-56504-6
Cyber-Physical-Security Model for Safety-Critical IoT Infrastructures
Creators: name: Bellekens, Xavier
name: Atkinson, Robert
name: Seeam, Amar
name: Tachtatzis, Christos
name: Andonovic, Ivan
name: Nieradzinska, Kamila
Publisher: name: figshare
Year: 2016
Subjects: subject: 80503 Networking and Communications
subject: FOS: Computer and information sciences
subject: 100503 Computer Communications Networks
subject: FOS: Electrical engineering, electronic engineering, information engineering

description: The Internet of Things (IoT) and the number of sensors integrated within safety critical environments is increasing exponentially. System designers employ off-the-shelf hardware to reduce development time and cost, however, the early adoption of consumer hardware and software raises numerous security questions. Several successful attacks and threats to critical infrastructures have been reported. This paper reviews safety-critical applications in aviation, connected cars and power plants. An engineering development roadmap is proposed with cyber-security in mind from “cradle-to-grave” rather than an afterthought. The development roadmap introduces a cybersecurity review at each design step to strengthen the robustness of IoT hardware and software. However, considering these systems have an extremely long lifetime (>20 years), secure maintenance and integrity of ageing infrastructure is usually a secondary consideration. The paper proposes the use of a cyclic cyberphysical security model after system commissioning that allows knowledge transfer between regulatory bodies through sharing of best practices. The sharing will enable system operators to identify exploits encountered from other industries and maintain high security levels and improve the IoT architectures.
GLoP: Enabling Massively Parallel Incident Response Through GPU Log Processing
Creators: name: Bellekens, Xavier
name: Kirkham, Tony
name: Atkinson, Robert
name: Tachtatzis, Christos
name: Renfrew, Craig
Publisher: name: figshare
Year: 2016
Subjects: subject: 80503 Networking and Communications
subject: FOS: Computer and information sciences
subject: 100503 Computer Communications Networks
subject: FOS: Electrical engineering, electronic engineering, information engineering
subject: 80303 Computer System Security

description: Large industrial systems that combine services and applications, have become targets for cyber criminals and are challenging from the security, monitoring and auditing perspectives. Security log analysis is a key step for uncovering anomalies, detecting intrusion, and enabling incident response. The constant increase of link speeds, threats and users, produce large volumes of log data and become increasingly difficult to analyse on a Central Processing Unit (CPU). This paper presents a massively parallel Graphics Processing Unit (GPU) Log Processing (GLoP) library and can also be used for Deep Packet Inspection (DPI), using a prefix matching technique, harvesting the full power of off- the-shelf technologies. GLoP implements two different algorithm using different GPU memory and is compared against CPU counterpart implementations. The library can be used for processing nodes with single or multiple GPUs as well as GPU cloud farms. The results show throughput of 20 Gbps and demonstrate that modern GPUs can be utilised to increase the operational speed of large scale log processing scenarios, saving precious time before and after an intrusion has occurred.
Modular and Didactic Compiler Design with XML Inter-Phases Communication
Creators: name: Navas-López, Eduardo Adam
Publisher: name: arXiv
Year: 2022
Subjects: subject: Programming Languages (cs.PL)
subject: FOS: Computer and information sciences
subject: D.3.4; E.2

description: In Compiler Design courses, students learn how a program written in high level programming language and designed for humans understanding is systematically converted into low level assembly language understood by machines, through different representations. This article presents the design, educative characteristics and possibilities of a modular and didactic compiler for a Pascal-like programming mini-language that is super-set of Niklaus Wirth's PL/0. The main feature is that it implements the compiling phases in such a way that the information delivered to each next one may be reflected as an XML document, which can be studied separately. It is also shown that its design is suitable for being included as learning tool into compiler design courses. It is possible to implement a compiler in a high-level language like Python.
A Neuromorphic Spiking Neural Network Detects Epileptic High Frequency Oscillations in the Scalp EEG
Creators: name: Burelo, Karla
name: Ramantani, Georgia
name: Indiveri, Giacomo
name: Sarnthein, Johannes
Publisher: name: ETH Zurich
Year: 2021

description: Background: Interictal High Frequency Oscillations (HFO) are measurable in scalp EEG. This has aroused interest in investigating their potential as biomarkers of epileptogenesis, seizure propensity, disease severity, and treatment response. The demand for therapy monitoring in epilepsy has kindled interest in compact wearable electronic devices for long- term EEG recording. Spiking neural networks (SNN) have been shown to be optimal architectures for being embedded in compact low-power signal processing hardware.

Methods: We analyzed 20 scalp EEG recordings from 11 patients with pediatric focal lesional epilepsy. We designed a custom SNN to detect events of interest (EoI) in the 80-250 Hz ripple band and reject artifacts in the 500-900 Hz band.

Results: We identified the optimal SNN parameters to automatically detect EoI and reject artifacts. The occurrence of HFO thus detected was associated with active epilepsy with 80% accuracy. The HFO rate mirrored the decrease in seizure frequency in 8 patients (p = 0.0047). Overall, the HFO rate correlated with seizure frequency (rho = 0.83, p < 0.0001, Spearman’s correlation).

Conclusions: The fully automated SNN detected clinically relevant HFO in the scalp EEG. This is a further step towards non-invasive epilepsy monitoring with a low-power wearable device.
description: Research Square
description: ISSN:2693-5015
A Highly-Efficient Memory-Compression Scheme for GPU-Accelerated Intrusion Detection Systems
Creators: name: Bellekens, Xavier
name: Atkinson, Robert
name: Tachtatzis, Christos
name: Kirkham, Tony
name: Renfrew, Craig
Publisher: name: figshare
Year: 2016
Subjects: subject: 80403 Data Structures
subject: FOS: Computer and information sciences
subject: 100604 Memory Structures
subject: FOS: Electrical engineering, electronic engineering, information engineering
subject: 80303 Computer System Security
subject: 80503 Networking and Communications
subject: 100503 Computer Communications Networks

description: Pattern Matching is a computationally intensive task used in many research fields and real world applications. Due to the ever-growing volume of data to be processed, and increasing link speeds, the number of patterns to be matched has risen significantly. In this paper we explore the parallel capabilities of modern General Purpose Graphics Processing Units (GPGPU) applications for high speed pattern matching. A highly compressed failure-less Aho-Corasick algorithm is presented for Intrusion Detection Systems on off-the-shelf hardware. This approach maximises the bandwidth for data transfers between the host and the Graphics Processing Unit (GPU). Experiments are performed on multiple alphabet sizes, demonstrating the capabilities of the library to be used in different research fields, while sustaining an adequate throughput for intrusion detection systems or DNA sequencing. The work also explores the performance impact of adequate prefix matching for alphabet sizes and varying pattern numbers achieving speeds up to 8Gbps and low memory consumption for intrusion detection systems.
The neuromorphic Mosaic: re-configurable in-memory small-world graphs
Creators: name: Dalgaty, Thomas
name: Moro, Filippo
name: De Pra, Alessio
name: Indiveri, Giacomo
name: Vianello, Elisa
name: Payvand, Melika
Publisher: name: ETH Zurich
Year: 2021

description: Thanks to their non-volatile and multi-bit properties, memristors have been extensively used as synaptic weight elements in neuromorphic architectures. However, their use to define and re-program the network connectivity has been overlooked. Here, we propose, implement and experimentally demonstrate Mosaic, a neuromorphic architecture based on a systolic array of memristor crossbars. For the first time, we use distributed non-volatile memristors not only for computation, but also for routing (i.e., to define the network connectivity). Mosaic is particularly well-suited for the implementation of re-configurable small-world graphical models, with dense local and sparse global connectivity - found extensively in the brain. We mathematically show that, as the networks scale up, the Mosaic requires less memory than in conventional memristor approaches. We map a spiking recurrent neural network on the Mosaic to solve an Electrocardiogram (ECG) anomaly detection task. While the performance is either equivalent or better than software models, the advantage of the Mosaic was clearly seen in respective one and two orders of magnitude reduction in energy requirements, compared to a micro-controller and address-event representation-based processor. Mosaic promises to open up a new approach to designing neuromorphic hardware based on graph-theoretic principles with less memory and energy.
description: Research Square
description: ISSN:2693-5015
Sub-Phases in the Superconducting State of CeIrIn$_5$ Revealed by Low Temperature $c$-axis Heat Transport
Creators: name: Shakeripour, H.
name: Tanatar, M. A.
name: Petrovic, C.
name: Taillefer, Louis
name: Taillefer, Louis
Publisher: name: arXiv
Year: 2022
Subjects: subject: Superconductivity (cond-mat.supr-con)
subject: Strongly Correlated Electrons (cond-mat.str-el)
subject: FOS: Physical sciences

description: Low-temperature (down to $\sim$ 50 mK) thermal conductivity measurements with the heat flow direction along the inter-plane tetragonal $c$-axis, $κ_c$, were used to study the superconducting state of heavy fermion CeIrIn$_5$. Measurements were performed in the magnetic fields both parallel to the heat flow direction, $H\parallel c$, and transverse to it, $H \parallel a$. Inter-plane heat conductivity in $H \parallel c$ configuration shows negligible initial increase with magnetic field and a rapid rise on approaching $H_{c2}$ from below, similar to the expectations for the superconducting gap without line nodes. This observation is in stark contrast to monotonic increase found in the previous in-plane heat transport measurements. In the configuration with the magnetic field breaking the tetragonal symmetry of the lattice, $H \parallel a$, $κ_c$ reveals non-monotonic evolution with temperature and magnetic field suggesting sub-phase boundary in the superconducting state. The characteristic temperature $T_{kink} \sim$ 0.07~K of the sub-boundary is well within the domain of bulk superconductivity $T_c \sim$ 0.4~K and $H_{c2}\sim$ 1.0~T. These results are consistent with a superconducting gap with an equatorial line node and polar point nodes, a gap symmetry of the D$_{4h}$ point group, for which magnetic field along the tetragonal plane breaks the degeneracy of the multi-component order parameter and induces a phase transition with nodal topology change.
description: 11 pages, 7 figures
Development of a two-axis cold atom interferometer system for acceleration and rotation measurements
Creators: name: Bilton, Nicola
Publisher: name: Imperial College London
Year: 2022

description: The use of atom interferometers within inertial navigation systems is very promising due to the potential for excellent bias stability and scale factor stability. This thesis presents the further development of a two axis atom interferometer system able to measure horizontal accelerations along two orthogonal axes and rotations along three orthogonal axes. An expression is derived for the phase of the interferometer in a moving vehicle near the surface of the rotating earth to better understand its use within an inertial navigation system. Rubidium 87 atoms are collected in a side-arm 2D MOT and pushed into the ultra-high vacuum chamber where they are captured in a 3D MOT. The atoms are then cooled to few μK through optical molasses and prepared in a magnetically insensitive ground state with a narrow velocity distribution using a series of microwave and optical pulses. Using a pi/2 - pi -pi/2 sequence of Raman pulses on an ensemble of atoms forms a Mach-Zehnder interferometer in which the atomic wavepackets from each arm interfere coherently at the output to measure accelerations or rotations. To improve the interferometer’s sensitivity, the noise and fringe contrast are opti- mised, resolving fringes for an interferometer having a sequence of three laser pulses separated by more than 25ms. Further analysis shows the interferometer’s shot-to-shot noise is 0.35μg, almost an order of magnitude smaller than the MEMS accelerometer. Furthermore, this points to the possibility of achieving a sensitivity of 100ng at 1s by increasing the repetition rate to 10 shots per second. By reversing the direction and/or the relative phase of the Raman beams, the noise of the interferometer associated with acceleration and rotation averages down steadily as t ^(-1/2) over more than 10 hours. The intrinsic phase bias of the interferometer is found to be to be only few tens of milliradians with a drift of around a milliradian over 10 hours. For an interferometer with pulses separated by 25ms this corresponds to a drift of only 10ng, a thousand times better than the drift of the MEMS accelerometer.
MEMSORN: Self-organization of an inhomogeneous memristive hardware for sequence learning
Creators: name: Payvand, Melika
name: Moro, Filippo
name: Nomura, Kumiko
name: Dalgaty, Thomas
name: Vianello, Elisa
name: Nishi, Yoshifumi
name: Indiveri, Giacomo
Publisher: name: ETH Zurich
Year: 2021
Subjects: MEMSORN
resistive memory
device-circuit-algorithm
neural network

description: Learning is a fundamental component for creating intelligent machines. Biological intelligence orchestrates synaptic and neuronal learning at multiple time-scales to self-organize populations of neurons for solving complex tasks. Inspired by this, we design and experimentally demonstrate an adaptive hardware architecture Memristive Self-organizing Spiking Recurrent Neural Network (MEMSORN). MEMSORN incorporates resistive memory (RRAM) in its synapses and neurons which configure their state based on Hebbian and Homeostatic plasticity respectively. For the first time, we derive these plasticity rules directly from the statistical measurements of our fabricated RRAM-based neurons and synapses. These “technologically plausible” learning rules exploit the intrinsic variability of the devices and improve the accuracy of the network on a sequence learning task by 30%. Finally, we compare the performance of MEMSORN to a fully-randomly set-up recurrent network on the same task, showing that self-organization improves the accuracy by more than 15%. This work demonstrates the importance of the device-circuit-algorithm co-design approach for implementing brain-inspired computing hardware.
description: Research Square
description: ISSN:2693-5015
Guia sobre estudios de libertad de operacion Aprovechamiento de la información en dominio público sobre medicamentos en Latinoamerica
Creators: name: Mendieta, Luisa Maria
Publisher: name: Zenodo
Year: 2024

description: Este manual se constituye como una herramienta que busca apoyar el desarrollo de una guía de libertad de operación para tecnologías sanitarias en salud.
A Study on the Power Parameter in Power Prior Bayesian Analysis
Creators: name: Han, Zifei
name: Ye, Keying
name: Wang, Min
Publisher: name: arXiv
Year: 2022
Subjects: subject: Methodology (stat.ME)
subject: FOS: Computer and information sciences

description: The power prior and its variations have been proven to be a useful class of informative priors in Bayesian inference due to their flexibility in incorporating the historical information by raising the likelihood of the historical data to a fractional power δ. The derivation of the marginal likelihood based on the original power prior,and its variation, the normalized power prior, introduces a scaling factor C(δ) in the form of a prior predictive distribution with powered likelihood. In this paper, we show that the scaling factor might be infinite for some positive δ with conventionally used initial priors, which would change the admissible set of the power parameter. This result seems to have been almost completely ignored in the literature. We then illustrate that such a phenomenon may jeopardize the posterior inference under the power priors when the initial prior of the model parameters is improper. The main findings of this paper suggest that special attention should be paid when the suggested level of borrowing is close to 0, while the actual optimum might be below the suggested value. We use a normal linear model as an example for illustrative purposes.
Experimental Effects of Passenger on Motorcycle Lateral Stability
Creators: name: J. Brendelson
name: J. Sadauckas
Publisher: name: figshare
Year: 2016
Subjects: subject: Mechanical Engineering
subject: FOS: Mechanical engineering
subject: 91106 Special Vehicles
subject: FOS: Environmental engineering

description: A proceedings paper of BMD 2016.

While many previously published works examine motorcycle lateral stability through simulation, current literature incorporating the results of motorcycle lateral stability testing is rather limited. Further, the documented effects of a passenger on weave and wobble vibration modes
has not been published. While conventional wisdom in the public domain suggests that the addition of a passenger benefits weave mode, the effect of a passenger on wobble mode has received even less scrutiny. Unlike bicycles, the majority of motorcycles sold to consumers incorporate
accommodations for carrying a passenger. The current work presents the analysis of experimental weave and wobble stability test data collected with a rider and luggage, with and without a passenger on a touring motorcycle. The Stochastic Subspace Identification (SSI) method has been used to quantify weave mode content and further utilized to examine the wobble mode content. A brief review of the test and analysis protocols is presented followed by the results of
the analysis. For the motorcycle tested, the results confirm the stabilizing effect of passenger on weave mode and suggest little effect on the wobble mode. Although stochastic excitation has previously proven effective for weave identification its usage and limitations for wobble are examined.
STIs as a cause of infertility
Creators: name: START Center
name: Hicks, Sarah
name: Mulugeta, Abigail
name: Bih Moki-Suh
name: Wyckoff, Elizabeth
name: McClelland, Scott R.
name: Stewart, Barclay
Publisher: name: Gates Open Research
Year: 2024

description: The START team conducted literature reviews of studies assessing the association between five key STIs (chlamydia, gonorrhea, T. vaginalis, M. genitalium, and syphilis) and both PID and infertility. The literature was also reviewed to identify the prevalence of infertility and the five key STIs as well as evidence of an association between PID and infertility directly. This series of literature reviews was used to generate data visualizations and a ranked list of the STIs in terms of importance and relevance for the Gates Foundation's WHI PST strategy.
Reframing the HPV vaccine
Creators: name: START Center
name: Zane, Gregory
name: Hicks, Sarah
name: Sutton, Anna
name: Madalitso Khwepeya
name: Stewart, Barclay
Publisher: name: Gates Open Research
Year: 2024

description: The START Center performed literature reviews to understand the underlying barriers to parental acceptance of the HPV vaccine for their adolescent daughters aged 9-14 years, assess the relationship between HPV vaccination and increased sexual activity in adolescence, and understand if parental amenability to the HPV vaccine for their daughters changed if the vaccine was aligned with cervical cancer prevention rather than with sexual and reproductive health.