bluuebunny/crossref_metadata_2025_split huggingface dataset:
Extreme Compression for Large Scale Data Store
Authors: Lauret Jérôme , Gonzalez Juan , Van Buren Gene , Nuñez Rafael , Canal Philippe , Naumann Axel 
Published: November 2020

For the last 5 years Accelogic pioneered and perfected a radically new theory of numerical computing codenamed “Compressive Computing”, which has an extremely profound impact on real-world computer science [1]. At the core of this new theory is the discovery of one of its fundamental theorems which states that, under very general conditions, the vast majority (typically between 70% and 80%) of the bits used in modern large-scale numerical computations are absolutely irrelevant for the accuracy of the end result. This theory of Compressive Computing provides mechanisms able to identify (with high intelligence and surgical accuracy) the number of bits (i.e., the precision) that can be used to represent numbers without affecting the substance of the end results, as they are computed and vary in real time. The bottom line outcome would be to provide a state-of-the-art compression algorithm that surpasses those currently available in the ROOT framework, with the purpose of enabling substantial economic and operational gains (including speedup) for High Energy and Nuclear Physics data storage/analysis. In our initial studies, a factor of nearly x4 (3.9) compression was achieved with RHIC/STAR data where ROOT compression managed only x1.4.
In this contribution, we will present our concepts of “functionally lossless compression”, have a glance at examples and achievements in other communities, present the results and outcome of our current, ongoing R&D, as well as present a high-level view of our plan to move forward with a ROOT implementation that would deliver a basic solution readily integrated into HENP applications. As a collaboration of experimental scientists, private industry, and the ROOT Team, our aim is to capitalize on the substantial success delivered by the initial effort and produce a robust technology properly packaged as an open-source tool that could be used by virtually every experiment around the world as means for improving data management and accessibility.
Jupyter-based service for JUNO analysis
Authors: Lin Tao 
Published: November 2020

The JUNO (Jiangmen Underground Neutrino Observatory) is designed to determine the neutrino mass hierarchy and precisely measure oscillation parameters. The estimated data volume of raw data is about 2 PB/year. The event rate of reactor anti-neutrinos is about 60/day, while the event rate of background is about O(10) Hz. The challenge is the event correlation during the analysis, where the background events could not be discarded. In order to use big data techniques to search for rare events, a Jupyter-based interactive service is developed for JUNO analysis.
In this paper, an overview of this service is presented. The infrastructure is based on Jupyter and Kubernetes, which provides the user interface and resource management. In order to integrate the data processing framework and big data techniques, an index file is used as an intermediate file, which points to the interested events. Data processing framework SNiPER is used to select the candidate of neutrino signals and produce the index file. Apache Spark is then used to process such index file repeatedly with data cached in memory. With the index file produced from Spark and the complete event data files, SNiPER is used to process them and produce the final physics result. At the end of paper, the test-bed is presented and the testing result is shown.
Effective Dynamic Integration and Utilization of Heterogenous Compute Resources
Authors: Fischer Max , Giffels Manuel , Heiss Andreas , Kuehn Eileen , Schnepf Matthias , von Cube Ralf Florian , Petzold Andreas , Quast Günter 
Published: November 2020

Increased operational effectiveness and the dynamic integration of only temporarily available compute resources (opportunistic resources) becomes more and more important in the next decade, due to the scarcity of resources for future high energy physics experiments as well as the desired integration of cloud and high performance computing resources. This results in a more heterogenous compute environment, which gives rise to huge challenges for the computing operation teams of the experiments.At the Karlsruhe Institute of Technology (KIT) we design solutions to tackle these challenges. In order to ensure an efficient utilization of opportunistic resources and unified access to the entire infrastructure, we developed the Transparent Adaptive Resource Dynamic Integration System (TARDIS). A scalable multi-agent resource manager providing interfaces to provision as well as dynamically and transparently integrate resources of various providers into one common overlay batch system. Operational effectiveness is guaranteed by relying on COBalD – the Opportunistic Balancing Daemon and its simple approach of taking into account the utilization and allocation of the different resource types, in order to run the individual workflows on the best-suited resource respectively.In this contribution we will present the current status of integrating various HPC centers and cloud providers into the compute infrastructure at the Karlsruhe Institute of Technology as well as our experiences gained in a production environment.
EuroEXA Custom Switch: an innovative FPGA-based system for extreme scale computing in Europe
Authors: Biagioni Andrea , Cretaro Paolo , Frezza Ottorino , Lo Cicero Francesca , Lonardo Alessandro , Paolucci Pier Stanislao , Pontisso Luca , Simula Francesco , Vicini Piero 
Published: November 2020

EuroEXA is a major European FET research initiative that aims to deliver a proof-of-concept of a next generation Exa-scalable HPC platform. EuroEXA leverages on previous projects results (ExaNeSt, ExaNoDe and ECOSCALE) to design a medium scale but scalable, fully working HPC system prototype exploiting state-of-the-art FPGA devices that integrate compute accelerators and low-latency high-throughputnetwork.
Exascale-class systems are expected to host a very large number of computing nodes, from 104 up to 105, so that capability and performances of the interconnect architecture are critical to achieve high computing efficiency at this scale. In this perspective, EuroEXA enhances the ExaNet architecture, inherited by the ExaNeSt project, and introduces a multi-tier, hybrid topology network built on top of an FPGA-integrated Custom Switch that provides high throughput and low inter-node traffic latency for the different layers of the network hierarchy.
Deployment of a few testbeds is planned, with incremental complexity and equipped with complete software stack and runtime environment, to support the integration and test of the network design and to allow for evaluation of system performance and scalability through benchmarks based on real HPC applications. Design and integration activities are ongoing and the first small scale prototype (50 nodes) is expected to be completed in fall 2020 followed, one year later, by the deployment of the larger prototype (250/500 nodes).
Künstliche Sauerstofftransporter können mehr als Sauerstoff liefern
Authors: Bäumler Hans 
Published: November 2020

ZusammenfassungZum gegenwärtigen Zeitpunkt ist in der EU und den USA kein artifizieller Sauerstofftransporter zugelassen. Hämoglobin-basierte Sauerstoff-Carrier (HBOC) sind bereits seit Jahrzehnten Gegenstand wissenschaftlicher Untersuchungen. Ein wesentliches Hindernis bei der Zulassung war bisher der Anspruch der Entwickler, einen universell einsetzbaren Blutersatz zu produzieren. Die Beschränkung auf eine Indikation scheint erfolgversprechender zu sein. Der Ansatz, nicht nur Sauerstoff von der Lunge zum Gewebe, sondern auch der Abtransport von Kohlendioxid vom Gewebe zur Lunge zu transportieren, der effektiver als mit Erythrozyten durchgeführt werden kann, erscheint besonders attraktiv. Aufgrund vielversprechender präklinischer sowie klinischer Untersuchungen besteht die Hoffnung, dass in absehbarer Zeit auch in der EU künstliche Sauerstofftransporter für therapeutische Zwecke zur Verfügung stehen werden.
Endoscopic prediction of submucosal invasion in Barrett’s cancer with the use of artificial intelligence: a pilot study
Authors: Ebigbo Alanna , Mendel Robert , Rückert Tobias , Schuster Laurin , Probst Andreas , Manzeneder Johannes , Prinz Friederike , Mende Matthias , Steinbrück Ingo , Faiss Siegbert , Rauber David , de Souza Luis A. , Papa João P. , Deprez Pierre H. , Oyama Tsuneo , Takahashi Akiko , Seewald Stefan , Sharma Prateek , Byrne Michael F. , Palm Christoph , Messmann Helmut 
Published: November 2020

Abstract
Background The accurate differentiation between T1a and T1b Barrett’s-related cancer has both therapeutic and prognostic implications but is challenging even for experienced physicians. We trained an artificial intelligence (AI) system on the basis of deep artificial neural networks (deep learning) to differentiate between T1a and T1b Barrett’s cancer on white-light images.
Methods Endoscopic images from three tertiary care centers in Germany were collected retrospectively. A deep learning system was trained and tested using the principles of cross validation. A total of 230 white-light endoscopic images (108 T1a and 122 T1b) were evaluated using the AI system. For comparison, the images were also classified by experts specialized in endoscopic diagnosis and treatment of Barrett’s cancer.
Results The sensitivity, specificity, F1 score, and accuracy of the AI system in the differentiation between T1a and T1b cancer lesions was 0.77, 0.64, 0.74, and 0.71, respectively. There was no statistically significant difference between the performance of the AI system and that of experts, who showed sensitivity, specificity, F1, and accuracy of 0.63, 0.78, 0.67, and 0.70, respectively.
Conclusion This pilot study demonstrates the first multicenter application of an AI-based system in the prediction of submucosal invasion in endoscopic images of Barrett’s cancer. AI scored equally to international experts in the field, but more work is necessary to improve the system and apply it to video sequences and real-life settings. Nevertheless, the correct prediction of submucosal invasion in Barrett’s cancer remains challenging for both experts and AI.
Effect of environmental temperature change on the neuromechanical function of the quadriceps muscle
Authors: Spillane Pádraig , Bampouras Theodoros M. 
Published: November 2020

AbstractThis study compared neuromechanical characteristics of voluntary (maximum voluntary contraction (MVC) peak torque, rate of torque development (RTD), voluntary activation (VA)) and electrically stimulated contractions (peak torque, RTD) when performed under the same temperature conditions. Twelve physically active males performed two isometric MVCs of the quadriceps muscle group in an isokinetic dynamometer. The MVCs were performed after lower limb submersion for 20 min in hot (40°C) or cold (10°C) water. A control MVC was performed in ambient room temperature (17 ± 0.7°C). Electrical twitches were delivered at rest pre‐MVC (Unpotentiated), during the plateau phase of the MVC (Superimposed) and post‐MVC (Potentiated). Peak torque for MVC, Unpotentiated and Potentiated was recorded. RTD was calculated for the MVC (at 50, 100, 150, 200 ms and peak torque time points), Unpotentiated and Potentiated twitches, while VA (using the central activation ratio method) was calculated. There was no significant change between conditions in MVC peak torque, MVC RTD, VA and (averaged) twitch peak torque (p > 0.05). Twitch RTD for the hot condition (1025.0 ± 163.0 N·m·s−1) was significantly higher (p = 0.003) than control (872.3 ± 142.9 N·m·s−1). In conclusion, environmental temperature changes, in the range examined, do not affect the ability to generate maximum torque or any of the RTD parameters in maximum voluntary isometric contractions. In contrast, increased heat results in higher RTD in electrically stimulated contractions, most likely induced by reduced contraction time. This has practical implications for the use of electromyostimulation for injury prevention.
Value of isokinetic strength testing for hamstring injury risk assessment: Should the ‘strongest’ mates stay ashore?
Authors: Pieters Dries , Witvrouw Erik , Wezenbeek Evi , Schuermans Joke 
Published: November 2020

AbstractAlthough isokinetic strength testing is commonly used in hamstring strain injury (HSI) rehabilitation and prevention, research findings concerning its predictive value remain inconclusive. Existing research focuses on peak torque (PT) and angle of PT, not analysing the torque behaviour throughout the testing range of motion (ROM). This study intended to assess the value of isokinetic curve evaluation in association with HSI. A sample of 116 male football players with and without a recent HSI history was submitted to bilateral isokinetic assessment of the knee and hip muscles. Raw isokinetic data were filtered and normalized prior to curve analysis submission in MATLAB. Torque development of each muscle group throughout the entire testing ROM was assessed using HSI history as an independent variable. Curve analysis revealed significant differences in torque behaviour in function of injury history. Players with an HSI history demonstrated significantly stronger concentric knee flexion and extension, eccentric knee extension and concentric hip extension patterns compared to the controls and their uninjured limb. HSI history was also associated with lower concentric hip flexion torques and lower mixed H:Q ratios compared to the control group and their contralateral limb. HSI history was associated with altered knee and hip muscle strength profiles, potentially due to isolated focus on local strength training in rehabilitation or mechanisms of neuromuscular inhibition. Because the differences in torque amplitude were range‐dependent and did not systematically concur with the point of PT achievement, isokinetic strength evaluation should most probably be conducted using curve analysis.
Evaluation of occupational radon exposure and comparison with residential radon exposure in Canada—a population-level assessment
Authors: Chen Jing 
Published: November 2020

Abstract
Radon is a naturally occurring radioactive gas and presents everywhere on the Earth at varying concentration in workplaces and at homes. With Canadian labour statistics, time statistics and more than 7600 long-term radon measurements in workplaces, occupational radon exposure is evaluated for all 20 job categories based on North American Industry Classification System. Results are compared with residential radon exposure based on more than 22 000 long-term radon tests conducted in Canadian homes. The average annual effective dose due to radon exposure in workplaces is 0.21 mSv, which is lower than the average annual effective dose of 1.8 mSv from radon exposure at home by a factor of eight. Due to relatively higher radon concentration in residential homes and longer time spent indoors at home, exposure at home contributes to 90% of workers’ total radon exposure (on average 1692 h in workplaces and 5852 h at homes). The analysis presented here is based on province-wide average radon exposures in various indoor and outdoor environments. Since the risk of developing lung cancer increases proportionally with increasing radon exposure, this evaluation indicates that on average reduction of radon levels in homes is very important and an effective way to reduce radon-induced lung cancers in Canada.
Adversarial domain adaptation with classifier alignment for cross-domain intelligent fault diagnosis of multiple source domains
Authors: Zhang Yongchao , Ren Zhaohui , Zhou Shihua , Yu Tianzhuang 
Published: November 2020

Abstract
Recently, most cross-domain fault diagnosis methods focus on single source domain adaptation. However, it is usually possible to obtain multiple labeled source domains in real industrial scenarios. The question of how to use multiple source domains to extract common domain-invariant features and obtain satisfactory diagnosis results is a difficult one. This paper proposes a novel adversarial domain adaptation with a classifier alignment method (ADACL) to address the issue of multiple source domain adaptation. The main elements of ADACL consist of a universal feature extractor, multiple classifiers and a domain discriminator. The parameters of the main elements are simultaneously updated via a cross-entropy loss, a domain distribution alignment loss and a domain classifier alignment loss. Under the framework of multiple loss cooperative learning, not only is the distribution discrepancy among all domains minimized, but so is the prediction discrepancy of target domain data among all classifiers. Two experimental cases on two source domains and three source domains verify that the ADACL can remarkably enhance the cross-domain diagnostic performance under diverse operating conditions. In addition, the diagnostic performance of different methods is extensively evaluated under noisy environments with a different signal-to-noise ratio.
A novel self-adaptive, multi-peak detection algorithm for blade tip clearance measurement based on a capacitive probe
Authors: Li Jian , Guo Guanghui , Duan Fajie , Niu Guangyue , Shao Xingchen , Zhou Cong , Shi Xiaojiang , Zhong Zhicai 
Published: November 2020

Abstract
Peak height detection is critical for blade tip clearance (BTC) measurement based on capacitive probes. However, the noise interference of the complicated working environments and instabilities of the rotation speed make high-precision peak detection a challenge. To solve this problem, a novel self-adaptive, multi-peak detection algorithm is proposed in this paper. The adaptive dual thresholds are used to identify the single peak and pre-estimate its peak parameters for the segmentation of the peak support region and the selection basis of the wavelet parameters. Then, based on the prior pre-estimation information, the wavelet matching is performed on the segmented single peak adaptively for eight times, and the derived mathematical models of the wavelet matching are fitted using the obtained matching coefficients to get precise peak parameters such as peak height and peak position. The blade tip clearance is calculated from the peak height through the calibration curve. The experiments show that the BTC measurement error of the proposed algorithm is under 15 μm, with the clearance ranging from

1
mm

to

3
mm

, and the proposed algorithm has preferable stability to comparable algorithms with the rotation speed ranging from 1000 rpm to 4000 rpm. This implies that the proposed algorithm has high anti-noise performance and good adaptability to rotation speed change.
Silver nanoparticles functionalized Paclitaxel nanocrystals enhance overall anti-cancer effect on human cancer cells
Authors: Muhammad Nazim , Zhao He , Song Wenjing , Gu Mingyang , Li Qian , Liu Yujia , Li Cheng , Wang Jihui , Zhan Honglei 
Published: November 2020

Abstract
For chemotherapeutic drugs, precise tumor-targeting and high anti-cancer efficiency is equally important in order to enhance chemotherapy and reverse drug resistance. The combination of multifunctional agents to achieve synergy should be a promising strategy. In our study, we have successfully developed novel multifunctionalized drug nanocrystals to realize co-delivery of the organic drug Paclitaxel (PTX), inorganic silver nanoparticles (AgNPs) and a tumor targeting agent. To be specific, PTX nanocrystals were first prepared as a template, then coated with polydopamine (PDA). The PDA layer was utilized as the connection bridge to produce and deposit AgNPs in situ, and provide sites for tumor-targeting peptide NR1 (RGDARF) grafting. As a result, these NR1/AgNP-decorated drug nanocrystals exhibited dramatically improved cellular uptake efficiency, in vitro anti-cancer activity and an anti-migratory effect against a variety of cancer cells, which was attributable to the synergistic, or at least additive, effect of the AgNPs and PTX, enhanced cellular uptake efficiency through NR1-receptor interaction, pH-responsive drug release and the nanoscaled nature. In particular, high anti-cancer activity and low side effects from these NR1/AgNP-decorated PTX nanocrystals were well balanced in terms of good selectivity and biocompatibility. Moreover, these novel drug nanocrystals displayed strong apoptotic-inducing potency, resulting in cell membrane lysis, nuclear damage, mitochondria dysfunction, excessive ROS release and double-stranded DNA breakage. The potential acting mechanism and molecular basis of these novel drug nanocrystals is relevant to the regulation of mitochondria-mediated apoptosis with a greater Bax-to-Bcl-2 ratio and the activation of pro-apoptotic P53 and caspase 3.
Probabilistic definition of the clinical target volume—implications for tumor control probability modeling and optimization
Authors: Bortfeld Thomas , Shusharina Nadya , Craft David 
Published: November 2020

Abstract
Evidence has been presented that moving beyond the binary definition of clinical target volume (CTV) towards a probabilistic CTV can result in better treatment plans. The probabilistic CTV takes the likelihood of disease spread outside of the gross tumor into account. An open question is: how to optimize tumor control probability (TCP) based on the probabilistic CTV. We derive expressions for TCP under the assumptions of voxel independence and dependence. For the dependent case, we make the assumption that tumors grow outward from the gross tumor volume. We maximize the (non-convex) TCP under convex dose constraints for all models. For small numbers of voxels, and when a dose-influence matrix is not used, we use exhaustive search or Lagrange multiplier theory to compute optimal dose distributions. For larger cases we present (1) a multi-start strategy using linear programming with a random cost vector to provide random feasible starting solutions, followed by a local search, and (2) a heuristic strategy that greedily selects which subvolumes to dose, and then for each subvolume assignment runs a convex approximation of the optimization problem. The optimal dose distributions are in general different for the independent and dependent models even though the probabilities of each voxel being tumorous are set to the same in both cases. We observe phase transitions, where a subvolume is either dosed to a high level, or it gets ‘sacrificed’ by not dosing it at all. The greedy strategy often yields solutions indistinguishable from the multi-start solutions, but for the 2D case involving organs-at-risk and the dependent TCP model, discrepancies of around 5% (absolute) for TCP are observed. For realistic geometries, although correlated voxels is a more reasonable assumption, the correlation function is in general unknown. We demonstrate a tractable heuristic that works very well for the independent models and reasonably well for the dependent models. All data are provided.
On the possibility of limit-cycle-state of peeling mode near stability boundary in the quiescent H-mode
Authors: Itoh Kimitaka , Kamiya Kensaku , Aiba Nobuyuki , Itoh Sanae-I 
Published: November 2020

Abstract
A model is proposed for the edge harmonic oscillation, in which the stationary coherent mode is sustained in the almost linear phase as has been observed in JT-60U. We study the coupled dynamics of the peeling mode amplitude and edge pressure gradient. The limit cycle oscillation is predicted. The peeling mode (which is almost in the linear phase) is in a dynamical stationary state with amplitude modulation. In this model, the time scales for the change of parameters that specify magnetic structures (such as magnetic shear and edge plasma current) are assumed to be much slower, so that are decoupled from the limit cycle dynamics. The condition that the limit cycle state appears is shown. The oscillation frequency of the modulation is given by the hybrid mean of the typical growth rate of the peeling mode and the additional loss rate of pressure gradient by the peeling mode.