Miracle-dz/UniFAR-FTUs huggingface dataset:
Influence of cattle manure slurry application on the solubility of cadmium, copper, and zinc in a manured acidic, loamy sand soil

The concentrations of Cd, Cu, and Zn were evaluated bimonthly in the soil solution of five acidic loamy-sand soil plots during an 18-mo period, before and after application of 25 Mg ha⁻¹ cattle-manure slurry on top of the plots. pH, Cd, Zn, Cu, and Fe concentrations, electrical conductivity (EC), and dissolved organic carbon (DOC) were monitored in the soil-solution samples extracted from soil down to 80-cm depth. Dissolved metal concentrations ranged from 9 × 10⁻⁷ to 9 × 10⁻⁵ mmol m⁻³ for Cd, from 0.14 to 33 mmol m⁻³ for Zn, from 0.005 to 5 mmol m⁻³ for Cu, and from 0.54 to 4.6 × 10⁻³ mmol m⁻³ for Fe. Maximum soil solution concentrations of Cd (9 × 10⁻⁵ mmol m⁻³) and Zn (33 mmol m⁻³) were found between 0- and 40-cm depth 6 wk after cattle-manure slurry application; Cu and Fe concentrations were not elevated. Except for the peak concentrations of Cd and Zn following the cattle-manure slurry application, no statistically significant temporal trends in heavy metal concentrations were observed. Multiple linear regression analysis showed a positive correlation between the metal concentrations with DOC and a negative correlation with pH. High EC levels in topsoil layers, resulting from manuring were also found to increase metal concentrations. In the topsoil the Cd and Zn concentrations were affected by soil solution pH, and in the subsoil by DOC variations. The Cu concentration was affected by DOC variations in all soil layers. Supercentrifugation of selected subsoil samples from one sampling date removed 80% of the Cd, Zn, and Cu from the solution along with DOC and Fe. Cation-exchange column experiments as well as anodic stripping voltammetry measurements (speciation) of selected soil solution samples, showed that 30 to 70% of the dissolved Cu and all Cd was bound in relatively fast dissociating metal complexes. The observed variations in soil solution heavy metal concentration and speciation show that a distribution coefficient that describes the partition of metals over soil solution and solid phase is not a constant but depends on the physicochemical situation.
Synthesis and Characterization of Aryl Ethynyl Terminated Liquid Crystalline Oligomers and Their Cured Polymers

In this paper the synthesis and characterization of two series wholly aromatic, main-chain, reactive liquid crystalline oligomers is reported. Phenylethynyl end-capped oligomers, based on 4-hydroxybenzoic acid (HBA) and 6-hydroxy-2-naphthoic acid (HNA), were successfully synthesized using standard high-temperature melt-condensation techniques. Melt processable oligomers, with M n = 1000, 5000, 9000, and 13 000 g mol -1 , and oligomers with HBA or HNA concentrations as high as 95 mol % were prepared. All oligomers showed well-defined, homogeneous, nematic melt behavior over a broad temperature range. The phenylethynyl end-capped oligomers could be cured at 370 °C and formed films with excellent mechanical and thermal properties, and without losing the nematic order. Low molecular weight oligomers, i.e. M n 5000 g mol -1 , polymerize predominantly via chain extension chemistry. The fully cured nematic polymers exhibit glass-transition temperatures (Tg) up to 203 °C, as determined by DMTA measurements, and thermal stabilities (5 wt % loss) up to 500 °C in both air and nitrogen atmosphere. The cured 1000 and 5000 g mol -1 oligomers, with 95 mol % HBA or HNA compositions, show significant improvements in storage modulus at elevated temperatures as compared to their high-molecular weight counterparts. Rheology experiments showed that these reactive nematic oligomers are melt-stable for at least 30 min at 300 °C and exhibit complex melt viscosities |η*| as low as 1 Pa·s at 100 rad·s -1 . In all examples no loss of mesophase could be observed during chain extension or cross-linking, which indicates that phenylethynyl cross-link chemistry is completely compatible with mesophase formation.
NOM coprecipitation with solids formed during softening

The effect of precipitate characteristics on natural organic matter (NOM) removal during softening was evaluated by conducting jar tests with synthetic organic water under varying conditions—calcium only, magnesium only, and calcium plus magnesium—using humic substances extracted from Lake Austin, Texas. Results from these experiments showed that both calcium carbonate (CaCO3) and magnesium hydroxide [Mg(OH)2] adsorbed NOM during precipitation. Each solid was capable of achieving the same maximum percent removal under the conditions evaluated; however, a greater mass of CaCO3 was required to achieve the same degree of NOM removal. These research results have important implications for softening plants. First, such plants may be able to increase NOM removal by operating at a slightly higher lime dose, maintaining a minimum calcium concentration but avoiding Mg(OH)2 precipitation. Second, an increase in NOM removal with increasing initial magnesium concentration was attributed to the formation of Mg‐CaCO3 and suggests that some softening plants may be able to enhance NOM removal by adding magnesium. Third, results supported findings by other researchers that soda ash is detrimental to NOM removal and should be added to the second stage of a two‐stage softening process if NOM removal is to be maximized.
Common-spectrum process versus cross-correlation for gravitational-wave searches using pulsar timing arrays

The North American Nanohertz Observatory for Gravitational Waves (NANOGrav) has recently reported strong statistical evidence for a common-spectrum red-noise process for all pulsars, as seen in their 12.5-yr analysis for an isotropic stochastic gravitational-wave background. However, there is currently very little evidence for quadrupolar spatial correlations across the pulsars in the array, which is needed to make a confident claim of detection of a stochastic background. Here we give a frequentist analysis of a very simple signal+noise model showing that the current lack of evidence for spatial correlations is consistent with the magnitude of the correlation coefficients for pairs of Earth-pulsar baselines in the array, and the fact that pulsar timing arraysbare most-likely operating in the intermediate-signal regime. We derive analytic expressions that allow one to compare the expected values of the signal-to-noise ratios for both the common-spectrum and cross-correlation estimators.
Optimal Caching for Low Latency in Distributed Coded Storage Systems

Erasure codes have been widely considered as a promising solution to enhance data reliability at low storage costs. However, in modern geo-distributed storage systems, erasure codes may incur high data access latency as they require data retrieval from multiple remote storage nodes. This hinders the extensive application of erasure codes to data-intensive applications. This paper proposes novel caching schemes to achieve low latency in distributed coded storage systems. Assuming that future data popularity and network latency information are available, an offline caching scheme is proposed to explore the optimal caching solution for low latency. The proposed scheme categorizes all feasible caching decisions into a set of cache partitions, and then obtains the optimal caching decision through market clearing price for each cache partition. Furthermore, guided by the optimal scheme, an online caching scheme is proposed according to the measured data popularity and network latency information in real time, without the need to completely override the existing caching decisions. Both theoretical analysis and experiment results demonstrate that the online scheme can approximate the offline optimal scheme well with dramatically reduced computation complexity.
Appearance of objectivity for NV centers interacting with dynamically polarized nuclear environment

Quantum-to-classical transition still eludes a full understanding. Out of its multiple aspects, one has recently gained an increased attention—the appearance of objective world out of the quantum. One particular idea is that objectivity appears thanks to specific quantum state structures formation during the evolution, known as spectrum broadcast structures (SBS). Despite that quite some research was already performed on this strong and fundamental form of objectivity, the practical realization of SBS in a concrete physical medium has not been explicitly analyzed so far. In this work, we study the possibility to simulate objectivization process via SBS formation using widely studied nitrogen-vacancy centers in diamonds. Assuming achievable limits of dynamical polarization technique, we show that for high, but experimentally viable polarizations (p > 0.5) of nuclear spins and for magnetic fields lower than ≈20 G the state of the NV center and its nearest polarized environment approaches an SBS state reasonably well.
Plasma performance and operational space without ELMs in DIII-D

A database of DIII-D plasmas without edge-localized modes (ELMs) compares the operating space and plasma performance of stationary no-ELM regimes found in conventional tokamaks: ELM suppression with resonant magnetic perturbations (RMPs), quiescent H-mode (QH, including wide-pedestal variant), improved confinement mode (I-mode), enhanced D-alpha H-mode (EDA-H), conventional low-confinement mode (L-mode), and negative triangularity L-mode (Neg-D). Operational space is documented in terms of engineering and physics parameters, revealing divergent constraints for each regime. Some operational space discriminants (such as pedestal collisionality) are well known, while others, such as low torque & safety factor, or high power & density, are less commonly emphasized. Normalized performance (confinement quality and normalized pressure) also discriminate the no-ELM regimes and favor the regimes tolerant to power in DIII-D: RMP, QH, and Neg-D. Absolute performance (volume-averaged pressure p , confinement time τ, and triple product pτ ) also discriminates no-ELM regimes and is found to rise linearly with IaB (a metric for the magnetic configuration strength, the product of current I, minor radius a, and field B that has units of force), and also benefits from tolerance to power. The highest normalized performance using the metric pτ/IaB is found in QH and RMP regimes. Focusing on ITER-shaped no-ELM plasmas, Q = 10 at 15 MA scaled global performance is met with some metrics ( βNH98/q952 , H983/βNq952 ), but not others ( βNH89/q952 , pτ/IaB ), and only thus far at high torque (β N is normalized pressure, H 89,98 is the confinement quality by scaling law, and q 95 is the safety factor). Though comparable QH and RMP performance is found, the pedestal pressure (p ped ≈ 2 p e,ped ) is very different. p ped in RMP plasmas is relatively low, and the best performance is found with a high core p fraction alongside high core rotation, consistent with an ExB shear confinement enhancement. p ped of QH plasmas is significantly higher than RMP, and QH performance does not correlate with core rotation. However, the highest QH p ped are found with high carbon fraction. While normalized performance of Neg-D plasmas is comparable to QH and RMP plasmas, the absolute confinement of Neg-D is lower, owing to low elongation and low p ped achieved thus far. Considering integration with electron cyclotron heating, the operational space for RMP and QH plasmas narrow, while that for EDA-H plasmas open, and the high p ped / p regimes (EDA-H and QH) preserve the highest performance. Only the EDA-H, Neg-D, and L-mode scenarios have approached divertor-compatible high separatrix density conditions, with Neg-D preserving the highest performance owing to its compatibility with both high power and density. Comparison to ELMing plasmas highlighted in the literature finds a clearer pedestal correlation to plasma performance, but also reveals that the peak performance of plasmas without ELMs is significantly lower in DIII-D, owing to limits in operational space accessed so far without ELMs.
FloodNet: A High Resolution Aerial Imagery Dataset for Post Flood Scene Understanding

Visual scene understanding is the core task in making any crucial decision in any computer vision system. Although popular computer vision datasets like Cityscapes, MS-COCO, PASCAL provide good benchmarks for several tasks (e.g. image classification, segmentation, object detection), these datasets are hardly suitable for post disaster damage assessments. On the other hand, existing natural disaster datasets include mainly satellite imagery which has low spatial resolution and a high revisit period. Therefore, they do not have a scope to provide quick and efficient damage assessment tasks. Unmanned Aerial Vehicle (UAV) can effortlessly access difficult places during any disaster and collect high resolution imagery that is required for aforementioned tasks of computer vision. To address these issues we present a high resolution UAV imagery, FloodNet, captured after the hurricane Harvey. This dataset demonstrates the post flooded damages of the affected areas. The images are labeled pixel-wise for semantic segmentation task and questions are produced for the task of visual question answering. FloodNet poses several challenges including detection of flooded roads and buildings and distinguishing between natural water and flooded water. With the advancement of deep learning algorithms, we can analyze the impact of any disaster which can make a precise understanding of the affected areas. In this paper, we compare and contrast the performances of baseline methods for image classification, semantic segmentation, and visual question answering on our dataset. FloodNet dataset can be downloaded from here: https://github.com/BinaLab/FloodNet-Supervised_v1. 0.
The dynamics of dense particles in vertical channel flows: gravity, lift and particle clusters

The dynamics of dense finite-size particles in vertical channel flows of Newtonian and viscoelastic carrier fluids are examined using particle resolved simulations. Comparison to neutrally buoyant particles in the same configuration highlights the effect of settling. The particle volume fraction is $5\%$, and a gravity field acts counter to the flow direction. Despite a modest density ratio ($\rho_r = 1.15$), qualitative changes arise due to the relative velocity between the particle and fluid phases. While dense particles are homogeneously distributed in the core of the channel, the mean concentration profile peaks in the near-wall region due to a competition between shear- and rotation-induced lift forces. These forces act in the cross-stream directions, and are analyzed by evaluating conditional averages along individual particle trajectories. The correlation between the angular and translational velocities of the particles highlights the significance of the Magnus lift force in both the spanwise and wall-normal directions. The collective behaviour of the particles is also intriguing. Using a \Voro analysis, strong clustering is identified in dense particles near the wall, which is shown to alter their streamwise velocities. This clustering is attributed to the preferential transport of aggregated particles towards the wall. The practical implication of the non-uniformity of particle distribution is a significant increase in drag. When the carrier fluid is viscoelastic, the particle migration is enhanced which leads to larger stresses, thus negating the capacity of viscoelasticity to reduce turbulent drag.
On the stability of the infinite Projected Entangled Pair Operator ansatz for driven-dissipative 2D lattices

We present calculations of the time-evolution of the driven-dissipative XYZ model using the infinite Projected Entangled Pair Operator (iPEPO) method, introduced by [A. Kshetrimayum, H. Weimer and R. Orús, Nat. Commun. 8, 1291 (2017)]. We explore the conditions under which this approach reaches a steady state. In particular, we study the conditions where apparently converged calculations may become unstable with increasing bond dimension of the tensor-network ansatz. We discuss how more reliable results could be obtained.
Different Approaches Towards Vertical Track Irregularity Prediction - A Comparative Study

Railway systems require regular manual maintenance, a large part of which is dedicated to track deformation inspection. Such deformation might severely impact trains' runtime security, whereas such inspections remain costly as for both finance and manpower. Therefore, a more precise, efficient and automated approach to detect potential railway track deformation is in urgent needs. In this paper, we proposed an applicational framework for predicting vertical track irregularities. Our researches are based on large-scale real-world datasets produced by several operating railways in China. We explored several different sampling methods and compared traditional machine learning algorithms for time-series prediction with popular deep learning techniques. Different ensemble learning methods are also employed for further optimization. The conclusion is reached that neural networks turn out to be the most performant and accurate.
Modeling galaxies in redshift space at the field level

We develop an analytical forward model based on perturbation theory to predict the redshift-space galaxy overdensity at the field level given a realization of the initial conditions. We find that the residual noise between the model and simulated galaxy density has a power spectrum that is white on large scales, with size comparable to the shot noise. In the mildly nonlinear regime, we see a k2μ2 correction to the noise power spectrum, corresponding to larger noise along the line of sight and on smaller scales. The parametric form of this correction has been predicted on theoretical grounds before, and our simulations provide important confirmation of its presence. We have also modeled the galaxy velocity at the field-level and compared it against simulated galaxy velocities, finding that about 10% of the galaxies are responsible for half of the rms velocity residual for our simulated galaxy sample.
Retarded Green’s functions from rotating AdS black holes: Emergent 
CFT2
 and viscosity

Using the AdS/CFT correspondence we consider the retarded Green's function in the background of rotating near-extremal AdS$_4$ black holes. Following the canonical AdS/CFT dictionary into the asymptotic boundary we get a CFT$_3$ result. We also take a new route and zoom in on the near-horizon region, blow up this region and show that it yields a CFT$_2$ result. We argue that the decoupling of the near-horizon region is akin to the decoupling of the near-throat region of a D3-brane, which led to the original formulation of the AdS/CFT correspondence, thus implying that the Kerr/CFT correspondence follows as a decoupling of the standard AdS/CFT correspondence applied to rotating black holes. As a byproduct, we compute the shear viscosity to entropy density ratio for the strongly coupled boundary CFT$_3$, and find that it violates the $1 / (4 \pi)$ bound.
BinArray: A Scalable Hardware Accelerator for Binary Approximated CNNs

Deep Convolutional Neural Networks (CNNs) have become state-of-the art for classification tasks due to their superior accuracy. BinArray is a custom hardware accelerator for CNNs with binary approximated weights. The binary approximation used is a network compression technique that drastically reduces the number of multiplications required per inference with no or very little accuracy degradation. BinArray scales and allows to compromise between hardware resource usage and throughput by means of three design parameters transparent to the user. Furthermore, it is possible to select between high accuracy or throughput dynamically during runtime. BinArray has been optimized at the register transfer level and operates at 400 MHz as instruction-set processor within a heterogenous XC7Z045-2 FPGA-SoC platform. Experimental results show that BinArray scales to match the performance of other accelerators for different network sizes. Even for the largest MobileNet only 50% of the target device and only 96 DSP blocks are utilized.
Potential theory for a class of strongly degenerate parabolic operators of Kolmogorov type with rough coefficients

In this paper we develop a potential theory for strongly degenerate parabolic operators of the form L := ∇X · (A(X,Y, t)∇X) +X · ∇Y − ∂t, in unbounded domains of the form Ω = {(X,Y, t) = (x, xm, y, ym, t) ∈ R × R× R × R× R | xm > ψ(x, y, ym, t)}, where ψ is assumed to satisfy a uniform Lipschitz condition adapted to the dilation structure and the (non-Euclidean) Lie group underlying the operator L. Concerning A = A(X,Y, t) we assume that A is bounded, measurable, symmetric and uniformly elliptic (as a matrix in R). Beyond the solvability of the Dirichlet problem and other fundamental properties our results include scale and translation invariant boundary comparison principles, boundary Harnack inequalities and doubling properties of associated parabolic measures. All of our estimates are translationand scale-invariant with constants only depending on the constants defining the boundedness and ellipticity of A and the Lipschitz constant of ψ. Our results represent a version, for operators of Kolmogorov type with bounded, measurable coefficients, of the by now classical results of Fabes and Safonov, any several others, concerning boundary estimates for uniformly parabolic equations in (time-dependent) Lipschitz type domains. 2000 Mathematics Subject Classification. 35K65, 35K70, 35H20, 35R03.
Learning summary features of time series for likelihood free inference

There has been an increasing interest from the scientific community in using likelihood-free inference (LFI) to determine which parameters of a given simulator model could best describe a set of experimental data. Despite exciting recent results and a wide range of possible applications, an important bottleneck of LFI when applied to time series data is the necessity of defining a set of summary features, often hand-tailored based on domain knowledge. In this work, we present a data-driven strategy for automatically learning summary features from univariate time series and apply it to signals generated from autoregressive-moving-average (ARMA) models and the Van der Pol Oscillator. Our results indicate that learning summary features from data can compete and even outperform LFI methods based on hand-crafted values such as autocorrelation coefficients even in the linear case.