Miracle-dz/UniFAR-FTUs huggingface dataset:
Assessing saturated hydraulic conductivity from the dielectrically-predicted dry bulk density

Direct measurement of saturated hydraulic conductivity ( K s ) and dry bulk density ( r b ) is often laborious, time consuming, and expensive. Predicted dry bulk density ( r b-ADR ) using Amplitude Domain Reﬂectometry (ADR) probe has been employed to predict saturated hydraulic conductivity ( K s_ADR ) after a slight modification on the previously available Non-Similar Media Concept (NSMC) model. All the predicted values were validated using the corresponding measured data of 108 undisturbed samples for each of 0-15 and 15-30 cm soil depths observed. The proposed “Dielectric NSMC” model could describe r b_ADR dependencies of K s_ADR better than the other proceeding models for the both ranges of soil depths observed. The proposed model tended to slightly underestimate the measured saturated hydraulic conductivity ( K s_meas ) as implied from the negative value of mean difference (MD). The scattering pattern of data which was well supported by the small value of Root Mean Square Error (RMSE) and Relative Deviation to Average Measured Data (rRSME), however, suggested a small deviation of the K s_ADR from the K s_meas . Thus, the proposed model was considerably sufficient for characterizing r b_ADR dependencies of K s_ADR . Effectiveness and practical merit of the r b_ADR prediction rather than the conventional direct measurement of r b , in particular, might promote a certain advantage for smoother fitting parameters.
Constraining the density dependence of the symmetry energy with nuclear data and astronomical observations in the KIDS framework

The KIDS framework for the nuclear equation of state (EoS) and energy density functional (EDF) offers the possibility to explore symmetry-energy (SE) parameters such as J (value at saturation density), L (slope), Ksym (curvature) and so on independently of each other and of assumptions about the effective mass. Here we examine the performance of EoSs with different SE parameters in reproducing nuclear properties and astronomical observations in an effort to constrain especially L and Ksym or the droplet-model counterpart Ktau. Assuming a standard EoS for symmetric matter, we explore several points on the hyperplane of (J,L,Ksym or Ktau) values. For each point, the corresponding EDF parameters and a pairing parameter are obtained for applications in spherical even-even nuclei. This is the first application of KIDS EDFs with pairing correlations. The EoSs are tested successively on properties of closed-shell nuclei, along the Sn isotopic chain, and on astronomical observations, in a step-by-step process of elimination and correction. A small regime of best-performing parameters is determined. The results strongly suggest that Ksym is negative and no lower than -200MeV, that Ktau lies between roughly -400 and -300MeV and that L lies between 40 and 65MeV with L<55MeV more likely. Correlations between symmetry-energy parameters are critically discussed. Predictions for the position of the neutron drip line and the neutron skin thickness of selected nuclei are reported. They are only weakly affected by the choice of effective mass values. Parts of the drip line can be sensitive to the SE parameters. The results underscore the role of Ktau and of precise astronomical input. Better constraints are possible with precise fits to nuclear energies and, in the future, more-precise input from astronomy.
Familial aggregation of multiple sclerosis: Results from the national registry of the disease in Saudi Arabia

Background Multiple Sclerosis (MS) is a chronic CNS inflammatory disease commonly affecting young adults. Both genetics and environmental factors have been reported to have a role in pathophysiology of the disease. Objective This article aims to report familial nature and aspects of MS in Saudi Arabia. Method The study utilized data collected by the National Saudi MS Registry between 2015 and 2018; especially data relevant to the familial history of MS. SPSS 22 was used for all analysis and reporting. Statistical significance was set at p-value < 0.05. Results The registry included 20 hospitals and a total of 2516 patients from the different regions of Saudi Arabia with median age 32.00 (Range: 11–63) and 66.5% being female. About 12.8% of all registered patients reported a family history of MS (95%CI: 11.2-13.9). Reported parental consanguinity was significantly higher among patients with family history (FMS) (56.3%) compared to non-FMS patients (27.9%). 42.53% of FMS patients reported having siblings affected with MS (95%CI: 37.01–48.21), with more female siblings affected than males (63.4% vs 36.6% respectively). Conclusion Our Findings suggested that FMS was less prevalent than what was reported previously; however, parental consanguinity was significantly more prevalent among FMS patients than non-FMS. Our findings were in line with those reported in recent studies in the region, but lower than those reported by western countries indicating that increasing prevalence of MS in Saudi Arabia could be multifactorial and other environmental factors should be considered for understanding this recent rise in the prevalence of MS in Saudi Arabia.
Agronomic comparisons of sugarcane varieties derived from tissue culture (NovaCane®) and conventional propagation under rainfed conditions

ABSTRACT Tissue-cultured (TC) sugarcane (Saccharum spp.) plants have been shown to possess an altered phenotype (high tillering and thin stalks) relative to conventionally propagated sugarcane from setts (Conv) in the first vegetative propagation stage (Stage 1). It is currently unclear if these effects are genotype specific, and whether they would persist in ratoon crops and the subsequent vegetative propagation stage (Stage 2). A field experiment was conducted to ascertain growth and yield differences between TC and Conv plants for different varieties (N12, N31, N41, and N48) across two crops (plant and first ratoon), and two plant spacings (30 cm and 50 cm) in Stage 1. A second field experiment evaluated agronomic performance of TC and Conv crops in Stage 2, established at three planting rates (PRs) (low, medium, and high). Plants produced from TC in both Stage 1 and Stage 2 produced significantly higher number of stalks that were thinner compared with Conv plants for selected varieties. Cane and estimated recoverable crystal (ERC) yields were statistically similar between TC (Stage 1) and Conv plants, whereas TC (Stage 2) plants significantly improved cane and ERC yields compared with Conv Varieties responded differently to the TC process (N41 and N48 TC plants did not show phenotypic variation relative to Conv). Plant spacing (Stage 1) and PRs (Stage 2) had no significant effects on agronomic traits. The TC plants can be used as planting materials for commercial production without any negative effects on productivity. This is despite the persistence of the reduced stalk diameter and increased stalk population phenotype. Routine screening for phenotypic responses to TC is recommended to sensitize growers to expected phenotypic changes of new variety releases.
Variational methods for a singular SPDE yielding the universality of the magnetization ripple

The magnetization ripple is a microstructure formed in thin ferromagnetic films. It can be described by minimizers of a nonconvex energy functional leading to a nonlocal and nonlinear elliptic SPDE in two dimensions driven by white noise, which is singular. We address the universal character of the magnetization ripple using variational methods based on Γ‐convergence. Due to the infinite energy of the system, the (random) energy functional has to be renormalized. Using the topology of Γ‐convergence, we give a sense to the law of the renormalized functional that is independent of the way white noise is approximated. More precisely, this universality holds in the class of (not necessarily Gaussian) approximations to white noise satisfying the spectral gap inequality, which allows us to obtain sharp stochastic estimates. As a corollary, we obtain the existence of minimizers with optimal regularity.
Turbulent density and pressure fluctuations in the stratified intracluster medium

Turbulent gas motions are observed in the intracluster medium (ICM). The ICM is density-stratified, with the gas density being highest at the centre of the cluster and decreasing radially outwards. As a result of this, Kolmogorov (homogeneous, isotropic) turbulence theory does not apply to the ICM. The gas motions are instead explained by anisotropic stratified turbulence, with the stratification quantified by the perpendicular Froude number (Fr⊥). These turbulent motions are associated with density and pressure fluctuations, which manifest as perturbations in X-ray surface brightness maps of the ICM and as thermal Sunyaev–Zeldovich effect (SZ) fluctuations, respectively. In order to advance our understanding of the relations between these fluctuations and the turbulent gas velocities, we have conducted 100 high-resolution hydrodynamic simulations of stratified turbulence (2562 × 384–10242 × 1536 resolution elements), in which we scan the parameter space of subsonic rms Mach number ($\mathcal {M}$), Fr⊥, and the ratio of entropy and pressure scale heights (RPS = HP/HS), relevant to the ICM. We develop a new scaling relation between the standard deviation of logarithmic density fluctuations (σs, where s = ln (ρ/$\langle$ρ$\rangle$)), $\mathcal {M}$, and Fr⊥, which covers both the strongly stratified (Fr⊥ ≪ 1) and weakly stratified (Fr⊥ ≫ 1) turbulence regimes: $\sigma _{\rm s}^2=\ln (1+b^2\mathcal {M}^4+0.10/(\mathrm{Fr}_\perp +0.25/\sqrt{\mathrm{Fr}_\perp })^2\mathcal {M}^2R_{\rm PS})$, where b ∼ 1/3 for solenoidal turbulence driving studied here. We further find that logarithmic pressure fluctuations σ(ln P/ < P >) are independent of stratification and scale according to the relation $\sigma _{(\ln {\bar{P}})}^2=\ln (1+b^2\gamma ^2\mathcal {M}^4)$, where $\bar{P}=P/\left\langle P \right\rangle $ and γ is the adiabatic index of the gas. We have tested these scaling relations to be valid over the parameter ranges $\mathcal {M} = 0.01$–0.40, Fr⊥ = 0.04–10.0, and RPS = 0.33–2.33.
Enabling Efficient Cyber Threat Hunting With Cyber Threat Intelligence

Log-based cyber threat hunting has emerged as an important solution to counter sophisticated attacks. However, existing approaches require non-trivial efforts of manual query construction and have overlooked the rich external threat knowledge provided by open-source Cyber Threat Intelligence (OSCTI). To bridge the gap, we propose ThreatRaptor, a system that facilitates threat hunting in computer systems using OSCTI. Built upon system auditing frameworks, ThreatRaptor provides (1) an unsupervised, light-weight, and accurate NLP pipeline that extracts structured threat behaviors from unstructured OSCTI text, (2) a concise and expressive domain-specific query language, TBQL, to hunt for malicious system activities, (3) a query synthesis mechanism that automatically synthesizes a TBQL query for hunting, and (4) an efficient query execution engine to search the big audit logging data. Evaluations on a broad set of attack cases demonstrate the accuracy and efficiency of ThreatRaptor in practical threat hunting.
Unsupervised Multi-hop Question Answering by Question Generation

Obtaining training data for multi-hop question answering (QA) is time-consuming and resource-intensive. We explore the possibility to train a well-performed multi-hop QA model without referencing any human-labeled multi-hop question-answer pairs, i.e., unsupervised multi-hop QA. We propose MQA-QG, an unsupervised framework that can generate human-like multi-hop training data from both homogeneous and heterogeneous data sources. MQA-QG generates questions by first selecting/generating relevant information from each data source and then integrating the multiple information to form a multi-hop question. Using only generated training data, we can train a competent multi-hop QA which achieves 61% and 83% of the supervised learning performance for the HybridQA and the HotpotQA dataset, respectively. We also show that pretraining the QA system with the generated data would greatly reduce the demand for human-annotated training data. Our codes are publicly available at https://github.com/teacherpeterpan/Unsupervised-Multi-hop-QA.
Accelerating Training of Transformer-Based Language Models with Progressive Layer Dropping

Recently, Transformer-based language models have demonstrated remarkable performance across many NLP domains. However, the unsupervised pre-training step of these models suffers from unbearable overall computational expenses. Current methods for accelerating the pre-training either rely on massive parallelism with advanced hardware or are not applicable to language modeling. In this work, we propose a method based on progressive layer dropping that speeds the training of Transformer-based language models, not at the cost of excessive hardware resources but from model architecture change and training technique boosted efficiency. Extensive experiments on BERT show that the proposed method achieves a 24% time reduction on average per sample and allows the pre-training to be 2.5 times faster than the baseline to get a similar accuracy on downstream tasks. While being faster, our pre-trained models are equipped with strong knowledge transferability, achieving comparable and sometimes higher GLUE score than the baseline when pre-trained with the same number of samples.
Temporal Reasoning on Implicit Events from Distant Supervision

We propose TRACIE, a novel temporal reasoning dataset that evaluates the degree to which systems understand implicit events—events that are not mentioned explicitly in natural language text but can be inferred from it. This introduces a new challenge in temporal reasoning research, where prior work has focused on explicitly mentioned events. Human readers can infer implicit events via commonsense reasoning, resulting in a more comprehensive understanding of the situation and, consequently, better reasoning about time. We find, however, that state-of-the-art models struggle when predicting temporal relationships between implicit and explicit events. To address this, we propose a neuro-symbolic temporal reasoning model, SymTime, which exploits distant supervision signals from large-scale text and uses temporal rules to combine start times and durations to infer end times. SymTime outperforms strong baseline systems on TRACIE by 5%, and by 11% in a zero prior knowledge training setting. Our approach also generalizes to other temporal reasoning tasks, as evidenced by a gain of 1%-9% on MATRES, an explicit event benchmark.
Exploiting Heterogeneous Graph Neural Networks with Latent Worker/Task Correlation Information for Label Aggregation in Crowdsourcing

Crowdsourcing has attracted much attention for its convenience to collect labels from non-expert workers instead of experts. However, due to the high level of noise from the non-experts, a label aggregation model that infers the true label from noisy crowdsourced labels is required. In this article, we propose a novel framework based on graph neural networks for aggregating crowd labels. We construct a heterogeneous graph between workers and tasks and derive a new graph neural network to learn the representations of nodes and the true labels. Besides, we exploit the unknown latent interaction between the same type of nodes (workers or tasks) by adding a homogeneous attention layer in the graph neural networks. Experimental results on 13 real-world datasets show superior performance over state-of-the-art models.
Solubility Limit of Ge Dopants in AlGaN: A Chemical and Microstructural Investigation Down to the Nanoscale.

Attaining low-resistivity AlxGa1-xN layers is one keystone to improve the efficiency of light-emitting devices in the ultraviolet spectral range. Here, we present a microstructural analysis of AlxGa1-xN/Ge samples with 0 ≤ x ≤ 1, and a nominal doping level in the range of 1020 cm-3, together with the measurement of Ge concentration and its spatial distribution down to the nanometer scale. AlxGa1-xN/Ge samples with x ≤ 0.2 do not present any sign of inhomogeneity. However, samples with x > 0.4 display μm-size Ge crystallites at the surface. Ge segregation is not restricted to the surface: Ge-rich regions with a size of tens of nanometers are observed inside the AlxGa1-xN/Ge layers, generally associated with Ga-rich regions around structural defects. With these local exceptions, the AlxGa1-xN/Ge matrix presents a homogeneous Ge composition which can be significantly lower than the nominal doping level. Precise measurements of Ge in the matrix provide a view of the solubility diagram of Ge in AlxGa1-xN as a function of the Al mole fraction. The solubility of Ge in AlN is extremely low. Between AlN and GaN, the solubility increases linearly with the Ga mole fraction in the ternary alloy, which suggests that the Ge incorporation takes place by substitution of Ga atoms only. The maximum percentage of Ga sites occupied by Ge saturates around 1%. The solubility issues and Ge segregation phenomena at different length scales likely play a role in the efficiency of Ge as an n-type AlGaN dopant, even at Al concentrations where Ge DX centers are not expected to manifest. Therefore, this information can have direct impact on the performance of Ge-doped AlGaN light-emitting diodes, particularly in the spectral range for disinfection (≈260 nm), which requires heavily doped alloys with a high Al mole fraction.
Using machine learning to correct model error in data assimilation and forecast applications

The idea of using machine learning (ML) methods to reconstruct the dynamics of a system is the topic of recent studies in the geosciences, in which the key output is a surrogate model meant to emulate the dynamical model. In order to treat sparse and noisy observations in a rigorous way, ML can be combined with data assimilation (DA). This yields a class of iterative methods in which, at each iteration, a DA step assimilates the observations and alternates with a ML step to learn the underlying dynamics of the DA analysis. In this article, we propose to use this method to correct the error of an existing, knowledge‐based model. In practice, the resulting surrogate model is a hybrid model between the original (knowledge‐based) model and the ML model. We demonstrate the feasibility of the method numerically using a two‐layer, two‐dimensional, quasi‐geostrophic channel model. Model error is introduced by the means of perturbed parameters. The DA step is performed using the strong‐constraint 4D‐Var algorithm, while the ML step is performed using deep learning tools. The ML models are able to learn a substantial part of the model error and the resulting hybrid surrogate models produce better short‐ to mid‐range forecasts. Furthermore, using the hybrid surrogate models for DA yields a significantly better analysis than using the original model.
Exploring Memory Access Patterns for Graph Processing Accelerators

Recent trends in business and technology (e.g., machine learning, social network analysis) benefit from storing and processing growing amounts of graph-structured data in databases and data science platforms. FPGAs as accelerators for graph processing with a customizable memory hierarchy promise solving performance problems caused by inherent irregular memory access patterns on traditional hardware (e.g., CPU). However, developing such hardware accelerators is yet time-consuming and difficult and benchmarking is non-standardized, hindering comprehension of the impact of memory access pattern changes and systematic engineering of graph processing accelerators. In this work, we propose a simulation environment for the analysis of graph processing accelerators based on simulating their memory access patterns. Further, we evaluate our approach on two state-of-the-art FPGA graph processing accelerators and show reproducibility, comparablity, as well as the shortened development process by an example. Not implementing the cycle-accurate internal data flow on accelerator hardware like FPGAs significantly reduces the implementation time, increases the benchmark parameter transparency, and allows comparison of graph processing approaches.
A biologically plausible neural network for Slow Feature Analysis

Learning latent features from time series data is an important problem in both machine learning and brain function. One approach, called Slow Feature Analysis (SFA), leverages the slowness of many salient features relative to the rapidly varying input signals. Furthermore, when trained on naturalistic stimuli, SFA reproduces interesting properties of cells in the primary visual cortex and hippocampus, suggesting that the brain uses temporal slowness as a computational principle for learning latent features. However, despite the potential relevance of SFA for modeling brain function, there is currently no SFA algorithm with a biologically plausible neural network implementation, by which we mean an algorithm operates in the online setting and can be mapped onto a neural network with local synaptic updates. In this work, starting from an SFA objective, we derive an SFA algorithm, called Bio-SFA, with a biologically plausible neural network implementation. We validate Bio-SFA on naturalistic stimuli.
Optimizing Coverage and Capacity in Cellular Networks using Machine Learning

Wireless cellular networks have many parameters that are normally tuned upon deployment and re-tuned as the network changes. Many operational parameters affect reference signal received power (RSRP), reference signal received quality (RSRQ), signal-to-interference-plus-noise-ratio (SINR), and, ultimately, throughput. In this paper, we develop and compare two approaches for maximizing coverage and minimizing interference by jointly optimizing the transmit power and downtilt (elevation tilt) settings across sectors. To evaluate different parameter configurations offline, we construct a realistic simulation model that captures geographic correlations. Using this model, we evaluate two optimization methods: deep deterministic policy gradient (DDPG), a reinforcement learning (RL) algorithm, and multi-objective Bayesian optimization (BO). Our simulations show that both approaches significantly outperform random search and converge to comparable Pareto frontiers, but that BO converges with two orders of magnitude fewer evaluations than DDPG. Our results suggest that data-driven techniques can effectively self-optimize coverage and capacity in cellular networks.
Multiscale Score Matching for Out-of-Distribution Detection

We present a new methodology for detecting out-of-distribution (OOD) images by utilizing norms of the score estimates at multiple noise scales. A score is defined to be the gradient of the log density with respect to the input data. Our methodology is completely unsupervised and follows a straight forward training scheme. First, we train a deep network to estimate scores for levels of noise. Once trained, we calculate the noisy score estimates for N in-distribution samples and take the L2-norms across the input dimensions (resulting in an NxL matrix). Then we train an auxiliary model (such as a Gaussian Mixture Model) to learn the in-distribution spatial regions in this L-dimensional space. This auxiliary model can now be used to identify points that reside outside the learned space. Despite its simplicity, our experiments show that this methodology significantly outperforms the state-of-the-art in detecting out-of-distribution images. For example, our method can effectively separate CIFAR-10 (inlier) and SVHN (OOD) images, a setting which has been previously shown to be difficult for deep likelihood models.