Miracle-dz/UniFAR-FTUs huggingface dataset:
Improved Counting Statistics of an Ultrafine DMPS System

Differential mobility particle size spectrometers (DMPS) are widely used to measure the aerosol number size- 10 distribution. Especially during new particle formation (NPF) the dynamics of the ultrafine size-distribution determine the significance of the newly formed particles within the atmospheric system. A precision quantification of the size-distribution and derived quantities such as new particle formation and growth rates is therefore essential. However, size-distribution measurements in the sub-10 nm range suffer from high particle losses and are often derived from only a few counts in the DMPS system, making them subject to very high counting uncertainties. Here we show that a CPC (modified Airmodus A20) 15 with a significantly higher aerosol optics flow rate compared to conventional ultrafine CPCs can greatly enhance the counting statistics in that size-range. Using Monte Carlo uncertainty estimates, we show that the uncertainties of the derived formation and growth rates can be reduced from 10-20% down to 1% by deployment of the high statistics CPC on a strong NPF event day. For weaker events and hence lower number concentrations, the counting statistics can result in a complete breakdown of the growth rate estimate with relative uncertainties as high as 75%, while the improved DMPS still provides reasonable results 20 at 10% relative accuracy. In addition, we show that other sources of uncertainty are present in CPC measurements, which might become more important when the uncertainty from the counting statistics is less dominant. Altogether, our study shows that the analysis of NPF events could be greatly improved by the availability of higher counting statistics in the used aerosol detector of DMPS systems. is used and with a still significant reduction for the modified Airmodus A20 down to 7-8% relative uncertainty (for the 6 th May 2017). At very low J 3 (5 th May 2017, Fig. 7), the Monte Carlo distributions for the TSI 3776 data get skewed and the Monte Carlo results shows a bimodal distribution with unphysical GR values around 0 indicating problems with the automated GR fitting. The relative 280 uncertainty becomes as large as 75%. This shows that GR values derived at such low number concentrations and which such low counting statistics are not reliable. Only instrumentation which provides enough signal can be used: even though the modified Airmodus A20 relative uncertainty already becomes as large as 10%, this value is still lower than the relative uncertainty of GR 3-6 for the TSI dataset of a very strong NPF event day with almost two orders of magnitude higher J 3 .
Speedup Breadth-First Search by Graph Ordering

—Breadth-First Search (BFS) is a core graph algorithm that is widely used for graph analysis. As it is frequently used in many graph applications, improving the BFS performance is essential. In this paper, we present a graph ordering method that could reorder the graph nodes to achieve better data locality, thus, improving the BFS performance. Our method is based on an observation that the sibling relationships will dominate the cache access pattern during the BFS traversal. Therefore, we propose a frequency-based model to construct the graph order. First, we optimize the graph order according to the nodes’ visit frequency. Nodes with high visit frequency will be processed in priority. Second, we try to maximize the child nodes’ overlap layer by layer. As it is proved to be NP-hard, we propose a heuristic method that could greatly reduce the preprocessing overheads. We conduct extensive experiments on 16 real-world datasets. The result shows that our method could achieve comparable performance with the state-of-the-art methods while the graph ordering overheads are only about 1/15.
Modeling of non-structural carbohydrate dynamics by the spatially 1 explicitly individual-based dynamic global vegetation model SEIB- 2 DGVM (SEIB-DGVM-NSC

Forest dynamics need to be considered when estimating the global carbon budget. The 15 alteration of forest structure and function under a changing climate and expanding human activity 16 could lead to a reduction of forest canopy cover and a spread of lower-biomass ecosystems in 17 warm and dry regions. Non-structural carbohydrate (NSC) acts as a storage buffer between carbon 18 supplied by assimilation and carbon consumed by, inter alia, respiration, reproduction, and pests. 19 Estimation of NSC concentrations in a tree is very important for accurate projection of future 20 forest dynamics. We developed a new NSC module for incorporation into a spatially explicit, 21 individual-based, dynamic global vegetation model (SEIB-DGVM) to validate the simulated NSC 22 dynamics with observations. NSC pools were simulated in three plant organs: leaves, trunk, and 23 roots. The seasonal dynamics of the NSCs varied among plant species, and the sizes of the NSC 24 pools inferred from observations differed between the boreal, temperate, and tropical climates. 25 The NSC models were therefore validated for each of the three climatic regions at both point and 26 global scales to assess the performance of the models. The modeled NSCs showed good 27 agreement in seasonality with the observed NSCs at four sites—Canada (boreal), Austria and 28 Switzerland (temperate), and Panama (tropical)—and in mean values for three climate zones 29 derived from the global NSC dataset. The SEIB-DGVM-NSCv1.0 is expected to enable 30 simulation of biome shifts caused by the changes of NSC dynamics worldwide. These dynamics 31 will contribute to changes of not only the global carbon cycle but also of forest structure and 32 demography at a global scale.
SOFI for Plasmonics: Extracting Near-field Intensity in the Far-Field at High Density

Unlike normal fluorescent methods that use the intensity as a direct measurement of the localized enhanced field, we use blinking statistics of quantum dots (QDs). We have already shown that blinking gives a more accurate characterization of the near-field. When an emitter is situated close to a metallic surface, non-radiative pathways are opened up, leading to quenching of the exciton. Blinking statistics, however, is only minimally affected by quenching, and therefore can be used to probe emitters in close proximity to metallic surfaces. We have expanded our method (COFIBINS) to high densities using superresolution technique SOFI. A proof of principle for SOFI-COFIBINS is demonstrated with a defocused point spread function. The method is then applied to surface plasmon polaritons. SOFI-COFIBINS shows excellent agreement with the average fluorescence intensity.
availabil-Predicting milk protein responses and the requirement of metabolizable protein by lactating dairy cows

The objective of this study was to develop a modeling framework to predict milk protein yield responses to varying metabolizable protein (MP) supplies and to determine the requirement of MP by lactating dairy cows. The logistic curve was used to model milk protein yield while accounting for a variable efficiency of MP utilization and between-study variability. Models were developed with databases from 2 recently published meta-analyses and based on either total MP supply or MP supply available for milk production. All models provided reasonable fit to data, with root mean square prediction error ranging from 18 to 20% of the average milk protein yield. The estimated horizontal asymptotes were 1.17 (posterior SD = 0.02) and 1.55 (posterior SD = 0.06) in the 2 databases, suggesting that the limiting milk protein yield, as MP supply increases, converges to 1.17 or 1.55 kg/d in the environments determined by the 2 databases. The observed efficiencies ranged from 0.75 to 0.18 when total MP supply was used as the denominator and above 1 to 0.24 when the MP supply available for milk production was used as the denominator. The predicted efficiencies were in good agreement with the data, decreasing nonlinearly with the MP supply. The MP requirement was calculated with a function constructed with the inverse of the logistic model and modified at regions of maximum marginal efficiency and minimum second derivative. This strategy assumes that the MP solution, or the MP needed to predict a given protein yield in the fitted logistic curve, determines the MP requirement for maintenance and lactation. Requirements calculated with the independent variable as total MP supply refer to the total requirement of maintenance plus lactation, whereas the requirement from models based on MP supply available for milk production are referent to the MP required only for lactation. The requirements were, on average, slightly smaller than the ones predicted by the current Northern American feeding system for dairy cows protein yields greater currently recom-mended at high yields.
Self Supervised Pre-training for Large Scale Tabular Data

In this paper, we tackle the problem of self supervised pre-training of deep neural networks for large scale tabular data in online advertising. Self supervised learning has recently been very effective for pre-training representations in domains such as vision, natural language processing, etc. But unlike these, designing self supervised learning tasks for tabular data is inherently challenging. Tabular data can consist of various types of data with high cardinality and range of feature values especially in a large scale real world setting. To that end, we propose a self supervised pre-training strategy that utilizes Manifold Mixup to produce data augmentations for tabular data and perform reconstruction on these augmentations using noise contrastive estimation and mean absolute error losses, both of which are particularly suitable for large scale tabular data. We demonstrate its efficacy by evaluating on the problem of click fraud detection on ads to obtain an improvement of 9% over a supervised learning baseline and 4% over a contrastive learning experiment.
Full Length Research Paper Do peers matter for tax saving?

This study aims to investigate if peers’ tax-saving success influences a firm’s tax aggressiveness. Relying on the inter-dependence amongst firms, the proposition that a firm would change its tax payment decisions is when it observes that its peer group achieves tax-saving success. A peer group was defined as the five firms most similar in size to the focal firm in the same industry. The authors examine whether peers’ tax level motivates the focal firm to adjust its tax strategy by controlling the effect of industrial leaders, whose profit margins are ranked top three in the industry. It was found that firms tend to mimic the average tax performance of their peers by changing their own tax burden. Additionally, this peer effect in the tax setting is more pronounced for firms with higher tax burden, lower profitability and less cash hold, revealing the asymmetric responses of firms to their peers’ performances under the above-mentioned conditions. The results also survive numerous robustness tests, including alternative measures of tax avoidance, different industry classifications and instrumental tests. The researchers provide empirical evidence of peer effects in tax payment decisions. The findings also suggest a novel way to detect tax-avoiding activities, which are likely to happen in a clustered manner.
The tetraquark candidate ${Z}_{{c}}(3900)$ from dynamical lattice QCD simulations

The structure of the tetraquark candidate $Z_{c}(3900)$, which was experimentally reported in $e^+ e^-$ collisions, is studied by the s-wave meson-meson coupled-channel scattering on the lattice. The s-wave interactions among the $\pi J/\psi$, $\rho \eta_{c}$ and $D \bar{D}^{*}$ channels are derived from (2+1)-flavor dynamical QCD simulations at $m_{\pi}=410$--$700$ MeV. It is found that the interactions are dominated by the off-diagonal $\pi J/\psi$-$D \bar{D}^{*}$ and $\rho \eta_{c}$-$D \bar{D}^{*}$ couplings. With the interactions obtained, the s-wave two-body amplitudes and the pole position in the $\pi J/\psi$-$\rho \eta_{c}$-$D \bar{D}^{*}$ coupled-channel scattering are calculated. The results show that the $Z_{c}(3900)$ is not a conventional resonance but a threshold cusp. A semiphenomenological analysis with the coupled-channel interaction to the experimentally observed decay mode is also presented to confirm the conclusion.
ForamEcoGENIE 2.0: Incorporating symbiosis and spine traits into a trait-based global planktic foraminifera model

Planktic foraminifera are major marine calcifiers in the modern ocean regulating the marine inorganic carbon pump and generating marine fossil archives of past climate change. Some planktic foraminifera evolved spine and symbiosis, increasing functional trait diversity and expanded their ecological niches. Here we incorporate symbiosis and spine traits into the ForamEcoGENIE model, a trait-based model focusing on functional trait rather than individual species, to enable us to 10 study the importance of foraminifera biodiversity in the palaeoceanographic environment. We calibrated the modelled new traits using Latin Hypercube Sampling. We identified the best model run from an ensemble of 1200 runs compared with observations from global core-top, sediment trap, and plankton nets. The model successfully captures the global distribution and seasonal variation of the 4 major functional groups including dominance of the symbiont-obligate type in subtropical gyres and the symbiont-barren type in the productive subpolar oceans. The carbon export rate is correctly predicted for spinose 15 foraminifera, but the model overestimates the global mean biomass of each group by 8 times and global export rate of non-spinose foraminifera by 4 times. Both the observational bias and the model's limitation in linking biomass to export production likely contributes to the discrepancy. Our model approximates a 3.05 g m -2 yr -1 global mean foraminifer-derived calcite flux and 1.1 Gt yr -1 total calcite export, account for 19% of the global pelagic marine calcite budget within the lower range of modern calcite estimates. The calcite export is mostly derived from the symbiont-barren non-spinose group (39%) and the 20 symbiont-obligate spinose group (13%). Our model overcomes the lack of biodiversity in previous version and offers the potential to explore foraminifera ecology dynamics and its impact on biogeochemistry in modern, future and paleogeographic environments. fast computational time and widely applied to past climates: Palaeocene–Eocene Thermal Maximum (Ridgwell and Schmidt, 75 2010), Last Glacial Maximum (Rae et al., 2020) and Cretaceous-Paleogene boundary (Henehan et al., 2019). Such computational efficiency and abundant applications make ForamEcoGENIE easily applicable to a wide range of geological periods with direct links with seawater carbon chemistry and isotope tracers. In this study, we extend ForamEcoGENIE to resolve 3 more critical functional groups of planktic foraminifera by adding the traits of symbiosis and spines (tested in Grigoratou et al., 2021b). Thereby, we build a model that can explore foraminifer ecogroups in past climates (Ezard et al., 80 2011). We compare the model with three global observational data compilations (core-top, plankton net tow and sediment traps) and test its ability to produce surface biomass, organic carbon and calcite flux rate, and relative abundance distribution.
Regionalizing the sea-level budget with machine learning techniques

Abstract. Attribution of sea-level change to its different drivers is typically done using a sea-level budget approach. While the global mean sea-level budget is considered closed, closing the budget on a finer spatial scale is more complicated due to, for instance, limitations in our observational system and the spatial processes contributing to regional sea-level change. Consequently, the regional budget has been mainly analysed on a basin-wide scale. Here we investigate the sea-level budget at sub-basin scales, using two machine learning techniques to extract domains of coherent sea-level variability: a neural network approach (self-organizing map, SOM) and a network detection approach (δ-MAPS). The extracted domains provide more spatial detail within the ocean basins and indicate how sea-level variability is connected among different regions. Using these domains we can close, within 1σ uncertainty, the sub-basin regional sea-level budget from 1993–2016 in 100 % and 76 % of the SOM and δ-MAPS regions, respectively. Steric variations dominate the temporal sea-level variability and determine a significant part of the total regional change. Sea-level change due to mass exchange between ocean and land has a relatively homogeneous contribution to all regions. In highly dynamic regions (e.g. the Gulf Stream region) the dynamic mass redistribution is significant. Regions where the budget cannot be closed highlight processes that are affecting sea level but are not well captured by the observations, such as the influence of western boundary currents. The use of the budget approach in combination with machine learning techniques leads to new insights into regional sea-level variability and its drivers.
Gravity or turbulence? IV. Collapsing cores in out-of-virial disguise

We study the dynamical state of cores by using a simple analytical model, a sample of observational massive cores, and numerical simulations of collapsing massive cores. From the model, we find that, if cores are formed from turbulent compressions, they evolve from small to large column densities, increasing their velocity dispersion as they collapse, while they tend to equipartition between gravity and kinetic energy. From the observational sample, we find that: (a) cores with substantially different column densities in the sample do not follow a Larson-like linewidth-size relation. Instead, cores with higher column densities tend to be located in the upper-left corner of the Larson velocity dispersion-size diagram, a result predicted previously (Ballesteros-Paredes et al. 2011a). (b) The data exhibit cores with overvirial values. Finally, in the simulations we reproduce the behavior depicted by the model and observational sample: cores evolve towards larger velocity dispersions and smaller sizes as they collapse and increase their column density. However, collapsing cores appear to approach overvirial states within a free-fall time. The cause of this apparent excess of kinetic energy is an underestimation of the gravitational energy, due to the assumption that the gravitational energy is given by the energy of an isolated sphere of constant column density. This excess disappears when the gravitational energy is correctly calculated from the actual spatial mass distribution, where inhomogeneities, as well as the potential due to the mass outside of the core, also contribute to the gravitational energy. We conclude that the observed energy budget of cores in surveys is consistent with their non-thermal motions being driven by their self-gravity and in a hierarchical and chaotic collapse.
Dense clusters of primes in subsets

We prove a generalization of the author’s work to show that any subset of the primes which is ‘well distributed’ in arithmetic progressions contains many primes which are close together. Moreover, our bounds hold with some uniformity in the parameters. As applications, we show there are infinitely many intervals of length $(\log x)^{{\it\epsilon}}$ containing $\gg _{{\it\epsilon}}\log \log x$ primes, and show lower bounds of the correct order of magnitude for the number of strings of $m$ congruent primes with $p_{n+m}-p_{n}\leqslant {\it\epsilon}\log x$ .
Giant Molecular Cloud

We present 45-47 angular resolution maps at 50 sampling of the CO and CO J=1-0 emission toward a 1.39 × 1.33 region in the W51 H II region complex. These data permit the spatial and kinematic separation of several spectral features observed along the line of sight to W51, and establish the presence of a massive (1.2 × 10 M⊙), large (∆l × ∆b = 83pc × 114 pc) giant molecular cloud (GMC), defined as the W51 GMC, centered at (l, b, V )c ∼ (49.5◦,−0.2◦, 61 km s). A second massive (1.9 × 10 M⊙), elongated (136 pc × 22 pc) molecular cloud is found at velocities of ∼ 68 km s along the southern edge of the W51 GMC. Of the five radio continuum sources that classically define the W51 region, the brightest source at λ 6 cm (G49.5-0.4) is spatially and kinematically coincident with the W51 GMC and three (G48.9-0.3, G49.1-0.4, and G49.2-0.4) are associated with the 68 km s cloud. Published absorption line spectra indicate that the fifth prominent continuum source (G49.4-0.3) is located behind the W51 molecular cloud. The W51 GMC is among the upper 1% of clouds in the Galactic disk by size and the upper 5–10% by mass. While the W51 GMC is larger and more massive than any nearby molecular cloud, the average H2 column density is not unusual given its size and the mean H2 volume density is comparable to that in nearby clouds. The W51 GMC is also similar to other clouds in that most of the molecular mass is contained in a diffuse envelope that is not currently forming massive stars. We speculate that much of the massive star formation activity in this region has resulted from a collision between the 68 km s cloud and the W51 GMC. Current Address: California Institute of Technology, Department of Astronomy, MS 105-24, Pasadena, CA 91125; jmc@astro.caltech. edu
The Influence of Contextual Clues on Consumers’ Attitudes towards Products

—Display is the link between products and consumers in online shopping. Designing better displays for products is the main content that retailers are concerned in the event of communicating with consumers. Previous studies focused on how clues affect consumers through perceptions of quality and price, while few studies explored this problem from the combination of consumers’ visual activities and cognitive responses. This study focuses on the contextual clues of clothing and explores the influence of contextual clues richness on consumers’ attitudes to products in clothing displays through eye movement experimental data and questionnaire data. Studies have shown that moderately rich contextual clues can obtain the best product attitudes, and visual attention and cognitive response play an intermediary role in this process. This study enriches the research on the influence of multi-clues on consumers’ behaviors in the clothing field and provides guidance and suggestions for online retailers’ product displays design.
Impact of Portfolio Management to Balance the Implementation of Change Initiatives and the Maintenance of Business-as-Usual, While Optimizing Return on Investment

The industrial pace is changing very rapidly, and the competitiveness is increasing too with globalization. Each business organization is finding a solution to find the best measure to gain the best potential profit of the business ignoring the obstacles. The research article has highlighted major issues related to the Portugal management. The aim of this study is to guide the potential benefit of the business through portfolio management. Purpose of this article has highlighted the impact and implementation process giving the best solution to the business organizations. Keywords: Portfolio management (PPM), impact, PPM implementation