bluuebunny/crossref_metadata_2025_split huggingface dataset:
OxRAM + OTS optimization for binarized neural network hardware implementation
Authors: Minguet Lopez J , Hirtzlin T , Dampfhoffer M , Grenouillet L , Reganaz L , Navarro G , Carabasse C , Vianello E , Magis T , Deleruyelle D , Bocquet M , Portal J M , Andrieu F , Molas G 
Published: October 2021

Abstract
Low-power memristive devices embedded on graphics or central processing units logic core are a very promising non-von-Neumann approach to improve significantly the speed and power consumption of deep learning accelerators, enhancing their deployment on embedded systems. Among various non-ideal emerging neuromorphic memory devices, synaptic weight hardware implementation using resistive random-access memories (RRAMs) within 1T1R architectures promises high performance on low precision binarized neural networks (BNN). Taking advantage of the RRAM capabilities and allowing to substantially improve the density thanks to the ovonic threshold selector (OTS) selector, this work proposes to replace the standard 1T1R architecture with a denser 1S1R crossbar system, where an HfO2-based resistive oxide memory (OxRAM) is co-integrated with a Ge-Se-Sb-N-based OTS. In this context, an extensive experimental study is performed to optimize the 1S1R stack and programming conditions for extended read window margin and endurance characteristics. Focusing on the standard machine learning MNIST image recognition task, we perform offline training simulations in order to define the constraints on the devices during the training process. A very promising bit error rate of ∼10−3 is demonstrated together with 1S1R 104 error-free programming endurance characteristics, fulfilling the requirements for the application of interest. Based on this simulation and experimental study, BNN figures of merit (system footprint, number of weight updates, accuracy, inference speed, electrical consumption per image classification and tolerance to errors) are optimized by engineering the number of learnable parameters of the system. Altogether, an inherent BNN resilience to 1S1R parasitic bit errors is demonstrated.
Thermodynamics of wetting, prewetting and surface phase transitions with surface binding
Authors: Zhao Xueping , Bartolucci Giacomo , Honigmann Alf , Jülicher Frank , Weber Christoph A 
Published: October 2021

Abstract
In living cells, protein-rich condensates can wet the cell membrane and surfaces of membrane-bound organelles. Interestingly, many phase-separating proteins also bind to membranes leading to a molecular layer of bound molecules. Here we investigate how binding to membranes affects wetting, prewetting and surface phase transitions. We derive a thermodynamic theory for a three-dimensional bulk in the presence of a two-dimensional, flat membrane. At phase coexistence, we find that membrane binding facilitates complete wetting and thus lowers the wetting angle. Moreover, below the saturation concentration, binding facilitates the formation of a thick layer at the membrane and thereby shifts the prewetting phase transition far below the saturation concentration. The distinction between bound and unbound molecules near the surface leads to a large variety of surface states and complex surface phase diagrams with a rich topology of phase transitions. Our work suggests that surface phase transitions combined with molecular binding represent a versatile mechanism to control the formation of protein-rich domains at intra-cellular surfaces.
Statistical approach to quantum phase estimation
Authors: Moore Alexandria J , Wang Yuchen , Hu Zixuan , Kais Sabre , Weiner Andrew M 
Published: October 2021

Abstract
We introduce a new statistical and variational approach to the phase estimation algorithm (PEA). Unlike the traditional and iterative PEAs which return only an eigenphase estimate, the proposed method can determine any unknown eigenstate–eigenphase pair from a given unitary matrix utilizing a simplified version of the hardware intended for the iterative PEA (IPEA). This is achieved by treating the probabilistic output of an IPEA-like circuit as an eigenstate–eigenphase proximity metric, using this metric to estimate the proximity of the input state and input phase to the nearest eigenstate–eigenphase pair and approaching this pair via a variational process on the input state and phase. This method may search over the entire computational space, or can efficiently search for eigenphases (eigenstates) within some specified range (directions), allowing those with some prior knowledge of their system to search for particular solutions. We show the simulation results of the method with the Qiskit package on the IBM Q platform and on a local computer.
Improving stimulated Brillouin scattering mitigation in weakly-damped plasmas: from spectral dispersion to spectral distribution
Authors: Fusaro A. , Loiseau P. , Penninckx D. , Riazuelo G. , Collin R. 
Published: October 2021

Abstract
Optical smoothing techniques are widely used in large high-power laser facilities designed for inertial confinement fusion research, such as the Laser Mégajoule (LMJ) in France. Those techniques are of primordial interest because they reduce laser–plasma instabilities as, for example, the stimulated backward Brillouin scattering (SBS). The latter takes place in the hot plasmas produced inside a hohlraum by the ablation of high-Z or gold material where ion acoustic waves are weakly damped, easing SBS. However, despite the current optical smoothing implementation, like smoothing by spectral dispersion (SSD), SBS is not sufficiently mitigated and may also damage the laser optics by following back the laser path. Moreover, SSD limits the available energy and may produce unwanted amplitude modulations in the laser chain, resulting in an additional damage. Using spectral distribution with two colors (Duluc et al 2019 Phys. Rev. Appl.
12 054055) has proven to limit the latter damage, keeping the same efficiency for limiting SBS. In this study, we go a step forward and generalize this approach by exploring multi-color spectral distribution schemes and the role of dispersion by means of three-dimensional simulations. This study paves the road to new laser concepts that could tackle more efficiently the laser–plasma interaction issues leading to a better laser energy coupling.
On a simple verification test of codes for modelling of magnetohydrodynamic turbulence
Authors: Kulikov Igor , Chernykh Igor , Boronina Marina 
Published: October 2021

Abstract
In the paper results for modelling a characteristic pattern of the solar wind flow and its turbulent structure are presented. We use a gravitational magnetic hydrodynamic model, and for the model we have created an effective numerical method based on Godunov’s method and piecewise parabolic reconstruction of the solution. We consider the evolution of the MHD turbulence of the supersonic gas flow, where random strong transversal velocity disturbances are set, the problem is considered in the vertical magnetic field. For the formation of a reference test of the supersonic MHD turbulence evolution a detailed analysis of the flow is carried out.
Biomass estimation in mangrove forests: a comparison of allometric models incorporating species and structural information
Authors: Rahman Md Saidur , Donoghue Daniel N M , Bracken Louise J , Mahmood Hossain 
Published: October 2021

Abstract
Improved estimates of aboveground biomass (AGB) are required to improve our understanding of the productivity of mangrove forests to support the long-term conservation of these fragile ecosystems which are under threat from many natural and anthropogenic pressures. To understand how individual species affects biomass estimates in mangrove forests, five species-specific and four genus-specific allometric models were developed. Independent tree inventory data were collected from 140 sample plots to compare the AGB among the species-specific models and seven frequently used pan-tropical and Sundarbans-specific generic models. The effect of individual tree species was also evaluated using model parameters for wood densities (from individual trees to the whole Sundarbans) and tree heights (individual, plot average and plot top height). All nine developed models explained a high percentage of the variance in tree AGB (R
2 = 0.97–0.99) with the diameter at breast height and total height (H). At the individual tree level, the generic allometric models overestimated AGB from 22% to 167% compared to the species-specific models. At the plot level, mean AGB varied from 111.36 Mg ha−1 to 299.48 Mg ha−1, where AGB significantly differed in all generic models compared to the species-specific models (p  < 0.05). Using measured species wood density (WD) in the allometric model showed 4.5%–9.7% less biomass than WD from published databases and other sources. When using plot top height and plot average height rather than measured individual tree height, the AGB was overestimated by 19.5% and underestimated by 8.3% (p  < 0.05). The study demonstrates that species-specific allometric models and individual tree measurements benefit biomass estimation in mangrove forests. Tree level measurement from the inventory plots, if available, should be included in allometric models to improve the accuracy of forest biomass estimates, particularly when upscaling individual trees up to the ecosystem level.
Groundwater phosphorus concentrations: global trends and links with agricultural and oil and gas activities
Authors: Warrack Jessica , Kang Mary , von Sperber Christian 
Published: October 2021

Abstract
Although observations show that anthropogenic phosphorus (P) can reach groundwater supplies, there has been no comprehensive evaluation of P in groundwater at the global scale and P contamination from sources such as agriculture and oil and gas activities are poorly understood. We compile and analyze 161 321 groundwater P measurements in 12 different countries to determine the extent of P contamination at the global scale. We find that all 12 countries report groundwater samples with concentrations >0.1 mg P l−1, a concentration at which the risk for eutrophication of surface waters is high. In Canada and the United States (US), we perform an analysis of 1529 dissolved oxygen (DO) concentrations to determine the degree of association between DO and groundwater P measurements. For P concentrations <0.1 mg P l−1, we find a strong inverse relationship between DO and P. However, for P concentrations >0.1 mg P l−1, we find a weak inverse correlation, which suggests anthropogenic sources are responsible for elevated P concentrations in groundwater. To identify anthropogenic sources of P, we conduct an analysis on land use data and the 24 146 P concentrations in Canada and the US. Although we find that 12% (2899) of all P concentrations are >0.1 mg P l−1, 33% of P concentrations from P monitoring sites located on pastureland (managed grassland) report concentrations >0.1 mg P l−1. In Alberta and Ontario, we analyze P measurements with respect to their proximity to oil and gas wells and find the relationship to be inconclusive. Overall, we find a positive correlation with agriculture and elevated groundwater P concentrations, but additional data are needed to identify a relationship between oil and gas wells and elevated groundwater P concentrations. Characterizing the role of agriculture and oil and gas wells on groundwater P contamination can help regulators develop effective strategies to protect water quality and ecosystem health.
I, N Co-doped hierarchical micro/mesoporous carbon modified separator for enhanced electrochemical performances of lithium-sulfur batteries
Authors: Yang Feng , Huang Kai 
Published: October 2021

Abstract
Although the high discharging capacity and coulombic efficiency can be obtained by impregnating sulfur into various conductive porous hosts in lithium-sulfur (Li-S) batteries, the low sulfur loading content and volumetric specific energy diminish their advantages. To solve this problem, a modified layer is prepared by coating I and N co-doped hierarchical micro/mesoporous kelp bio-carbon on surface of the polypropylene separator. Furthermore, the composite porous carbon is obtained by one-step thermal pyrolysis of edible dried kelp. The modified layer on separator can effectively prevent shuttle of polysulfides due to the synergistic effect from the micro/mesoporous carbon structures (physisorption effect) and I and N co-doped electrochemical active sites in carbon matrix (chemisorption effect), allowing high capacity and high coulombic efficiency. In this study, we developed Li-S batteries with 80% sulfur loading content and the area loading is higher than 2.0 mg cm−2, which are better than previous published ones. In addition, the cathode electrode could stabilize at 760 mAh g−1 after 200 cycles at 0.5 C and delivers a high retention specific capacity of 498 mAh g−1 after 500 cycles with a slight capacity attenuation of 0.07% per cycle.
Switching of quantum synchronization in coupled optomechanical oscillators
Authors: Kalita Sampreet , Chakraborty Subhadeep , Sarma Amarendra K 
Published: October 2021

Abstract
We explore the phenomenon of quantum phase synchronization in two optomechanical oscillators, coupled either bidirectionally or unidirectionally to each other. We first show that irrespective of the configuration of the optomechanical oscillators, synchronization can be achieved, with a finite degree of quantum correlation. However, while looking at the variation of the synchronization against the frequency detuning of the two oscillators, we observe a profound effect of the directionality of the optical coupling. For instance, we find that when the two optomechanical cavities exchange photons bidirectionally, synchronization traces the classic Arnold tongue. Whereas, for the unidirectional configuration, synchronization exhibits a novel blockade-like behavior where finite detuning favors synchronization. We also observe a strong connection between synchronization blockade and synchronization phase transition.
Pathways of Microcirculatory Endothelial Dysfunction in Obstructive Sleep Apnea: A Comprehensive Ex Vivo Evaluation in Human Tissue
Authors: Gavrilin Mikhail A , Porter Kyle , Samouilov Alexandre , Khayat Rami N 
Published: October 2021

Abstract

Background
The mechanism and markers of cardiovascular disease (CVD) in obstructive sleep apnea (OSA) remain unknown. The microcirculation is the site of early changes in OSA patients who are free of CVD risk.

Methods
Patients with newly diagnosed moderate to severe OSA (n = 7) were studied before and 12 weeks after intensive treatment with continuous positive airway pressure (CPAP), along with weight and age matched controls (n = 7). Microcirculatory vessels were isolated from gluteal biopsies and changes in critical functional genes were measured.

Results
The following genes changed after 12 weeks of intensive CPAP therapy in the microcirculatory vessels: angiotensin receptor type 1 (AGTR-1) (11.6 (3.4) to 6 (0.8); P = 0.019); NADPH oxidase (NOX4) (0.85 (0.02) to 0.79 (0.11); P = 0.016); and dimethylarginine dimethylaminohydrolase (DDAH 1) (1 (0.31) to 0.55 (0.1); P = 0.028). Despite decreased nitric oxide (NO) availability as measured indirectly through brachial artery flow-mediated dilation, endothelial NO synthase (NOS3) did not change with CPAP. Other disease markers of OSA that changed with treatment in the microcirculation were endothelin, hypoxia inducible factor 1a, nuclear factor kappa B, interleukin-8, and interleukin-6.

Conclusions
In this ex vivo evaluation of the microcirculation of patients with OSA and no CVD risk, several pathways of CVD were activated supporting that OSA independently induces microcirculatory endothelial dysfunction and serving as disease-specific markers for future pharmacological targeting of OSA-related CVD risk. The findings support the role of renin–angiotensin activation and endothelial oxidative stress in the decreased microcirculatory NO availability in OSA.
LocalNgsRelate: a software tool for inferring IBD sharing along the genome between pairs of individuals from low-depth NGS data
Authors: Severson Alissa L , Korneliussen Thorfinn Sand , Moltke Ida 
Published: October 2021

AbstractMotivationInference of identity-by-descent (IBD) sharing along the genome between pairs of individuals has important uses. But all existing inference methods are based on genotypes, which is not ideal for low-depth Next Generation Sequencing (NGS) data from which genotypes can only be called with high uncertainty.ResultsWe present a new probabilistic software tool, LocalNgsRelate, for inferring IBD sharing along the genome between pairs of individuals from low-depth NGS data. Its inference is based on genotype likelihoods instead of genotypes, and thereby it takes the uncertainty of the genotype calling into account. Using real data from the 1000 Genomes project, we show that LocalNgsRelate provides more accurate IBD inference for low-depth NGS data than two state-of-the-art genotype-based methods, Albrechtsen et al. (2009) and hap-IBD. We also show that the method works well for NGS data down to a depth of 2×.Availability and implementationLocalNgsRelate is freely available at https://github.com/idamoltke/LocalNgsRelate.Supplementary informationSupplementary data are available at Bioinformatics online.
DeepMP: a deep learning tool to detect DNA base modifications on Nanopore sequencing data
Authors: Bonet Jose , Chen Mandi , Dabad Marc , Heath Simon , Gonzalez-Perez Abel , Lopez-Bigas Nuria , Lagergren Jens 
Published: October 2021

Abstract

Motivation
DNA methylation plays a key role in a variety of biological processes. Recently, Nanopore long-read sequencing has enabled direct detection of these modifications. As a consequence, a range of computational methods have been developed to exploit Nanopore data for methylation detection. However, current approaches rely on a human-defined threshold to detect the methylation status of a genomic position and are not optimized to detect sites methylated at low frequency. Furthermore, most methods use either the Nanopore signals or the basecalling errors as the model input and do not take advantage of their combination.

Results
Here, we present DeepMP, a convolutional neural network-based model that takes information from Nanopore signals and basecalling errors to detect whether a given motif in a read is methylated or not. Besides, DeepMP introduces a threshold-free position modification calling model sensitive to sites methylated at low frequency across cells. We comprehensively benchmarked DeepMP against state-of-the-art methods on Escherichia coli, human and pUC19 datasets. DeepMP outperforms current approaches at read-based and position-based methylation detection across sites methylated at different frequencies in the three datasets.

Availability and implementation
DeepMP is implemented and freely available under MIT license at https://github.com/pepebonet/DeepMP.

Supplementary information
Supplementary data are available at Bioinformatics online.
Carbon footprinting of carbon capture and -utilization technologies: discussion of the analysis of Carbon XPRIZE competition team finalists
Authors: Sleep Sylvia , Munjal Raghav , Leitch Michael , Extavour Marcius , Gaona Adriana , Ahmad Shah , Nishikawa Emily , Pitre Vaidehi , Psarras Peter , Ramirez Andrea , MacLean Heather L , Bergerson Joule A 
Published: October 2021

Abstract
Life cycle assessments (LCAs) of early-stage technologies can provide valuable insights about key drivers of emissions and aid in prioritizing research into further emissions-reduction opportunities. Despite this potential value, further development of LCA methods is required to handle the increased uncertainty, data gaps, and confidentially of early-stage data. This study presents a discussion of the life cycle carbon footprinting of technologies competing in the final round of the NRG COSIA Carbon XPRIZE competition—a US$20 million competition for teams to demonstrate the conversion of CO2 into valuable products at the scale of a small industrial pilot using consistent deployment conditions, boundaries, and methodological assumptions. This competition allowed the exploration of how LCA can be used and further improved when assessing disparate and early-stage technologies. Carbon intensity estimates are presented for two conversion pathways: (i) CO2 mineralization and (ii) catalytic conversion (including thermochemical, electrochemical, photocatalytic and hybrid process) of CO2, aggregated across teams to highlight the range of emissions intensities demonstrated at the pilot for individual life cycle stages. A future scenario is also presented, demonstrating the incremental technology and deployment conditions that would enable a team to become carbon-avoiding relative to an incumbent process (i.e. reducing emissions relative to a reference pathway producing a comparable product). By considering the assessment process across a diverse set of teams, conversion pathways and products, the study presents generalized insights about opportunities and challenges facing carbon capture and -utilization technologies in their next phases of deployment from a life cycle perspective.
Reacting to the 2008 crisis: Competitiveness performances of Southern Italy and CEE regions
Authors: De Vivo Paola , Rinaldi Caterina 
Published: October 2021

Abstract
The 2008 Global Financial Crisis influenced geo-economic dynamics in the EU, triggering a repositioning of less developed regions. This analysis examines whether their competitiveness changed after 2008, and why some regions improved in performance. Using ERCI data, we compare EU lagging regions at NUTS2 level, finding that the Central and Eastern European (CEE) regions reacted to the crisis better than Southern Italian ones. We find that the divergence in their competitiveness pathways depends on the scale of their financial resources and on institutional endowments and legacy, which in turn directly affects the implementation of regional policies.
Deception as a Bridging Concept in the Study of Disinformation, Misinformation, and Misperceptions: Toward a Holistic Framework
Authors: Chadwick Andrew , Stanyer James 
Published: October 2021

AbstractWe propose deception as a bridging concept that will enhance the study of misinformation, disinformation, and misperceptions. As we set it out here, the concept integrates insights from multiple social science disciplines and uniquely connects actors’ intentions, information, and attitudinal or behavioral outcomes. A focus on deception will enrich research that describes the existence of false and misleading information but stops short of identifying their influence. Equally, through its focus on how actors’ deceptive strategies are important in attempts to exercise power, it can augment the study of the cognitive and attitudinal biases that render people susceptible to misperceptions. We identify the main themes in the study of deception: media-systemic distortions in information supply; the relational interactions that both produce and activate cognitive biases; and the attributes, strategies, and techniques of deceptive entities. We conclude with a summary typology of 10 principal variables and their 57 focal indicators.