Miracle-dz/UniFAR-FTUs huggingface dataset:
Pattern-mixture models for categorical outcomes with non-monotone missingness

Although most models for incomplete longitudinal data are formulated within the selection model framework, pattern-mixture models have gained considerable interest in recent years [R.J.A. Little, Pattern-mixture models for multivariate incomplete data, J. Am. Stat. Assoc. 88 (1993), pp. 125–134; R.J.A. Lrittle, A class of pattern-mixture models for normal incomplete data, Biometrika 81 (1994), pp. 471–483], since it is often argued that selection models, although many are identifiable, should be approached with caution, especially in the context of MNAR models [R.J. Glynn, N.M. Laird, and D.B. Rubin, Selection modeling versus mixture modeling with nonignorable nonresponse, in Drawing Inferences from Self-selected Samples, H. Wainer, ed., Springer-Verlag, New York, 1986, pp. 115–142]. In this paper, the focus is on several strategies to fit pattern-mixture models for non-monotone categorical outcomes. The issue of under-identification in pattern-mixture models is addressed through identifying restrictions. Attention will be given to the derivation of the marginal covariate effect in pattern-mixture models for non-monotone categorical data, which is less straightforward than in the case of linear models for continuous data. The techniques developed will be used to analyse data from a clinical study in psychiatry.
AdVulCode: Generating Adversarial Vulnerable Code against Deep Learning-Based Vulnerability Detectors

Deep learning-based vulnerability detection models have received widespread attention; however, these models are susceptible to adversarial attack, and adversarial examples are a primary research direction to improve the robustness of the models. There are three main categories of adversarial example generation methods for source code tasks: changing identifier names, adding dead code, and changing code structure. However, these methods cannot be directly applied to vulnerability detection. Therefore, we propose the first study of adversarial attack on vulnerability detection models. Specifically, we utilize equivalent transformations to generate candidate statements and introduce an improved Monte Carlo tree search algorithm to guide the selection of candidate statements to generate adversarial examples. In addition, we devise a black-box approach that can be applied to widespread vulnerability detection models. The experimental results show that our approach achieves attack success rates of 16.48%, 27.92%, and 65.20%, respectively, in three vulnerability detection models with different levels of granularity. Compared with the state-of-the-art source code attack method ALERT, our method can handle models with identifier name mapping, and our attack success rate is 27.59% higher on average than ALERT.
MOF-Derived CoNi Nanoalloy Particles Encapsulated in Nitrogen-Doped Carbon as Superdurable Bifunctional Oxygen Electrocatalyst

Carbon-encapsulated transition metal catalysts have caught the interest of researchers in the oxygen reduction reaction (ORR) and the oxygen evolution reaction (OER) due to their distinctive architectures and highly tunable electronic structures. In this work, we synthesized N-doped carbon encapsulated with CoNi nanoalloy particles (CoNi@NC) as the electrocatalysts. The metal-organic skeleton ZIF-67 nanocubes were first synthesized, and then Ni2+ ions were inserted to generate CoNi-ZIF precursors by a simple ion-exchange route, which was followed by pyrolysis and with urea for the introduction of nitrogen (N) at a low temperature to synthesize CoNi@NC composites. The results reveal that ZIF-67 pyrolysis can dope more N atoms in the carbon skeleton and that the pyrolysis temperature influences the ORR and OER performances. The sample prepared by CoNi@NC pyrolysis at 650 °C has a high N content (9.70%) and a large specific surface area (167 m2 g−1), with a positive ORR onset potential (Eonset) of 0.89 V vs. RHE and half-wave potential (E1/2) of 0.81 V vs. RHE in 0.1 M KOH, and the overpotential of the OER measured in 1 M KOH was only 286 mV at 10 mA cm−2. The highly efficient bifunctional ORR/OER electrocatalysts synthesized by this method can offer some insights into the design and synthesis of complex metal-organic frameworks (MOFs) hybrid structures and their derivatives as functional materials in energy storage.
Optical and THz Galois diffusers

Binary surface reliefs with sub-wavelength features making up a pseudorandom pattern based on mathematical Galois fields GF(p^m) [1, 2] can scatter incoming waves into a large number of diffraction maxima within a huge solid angle. A one-dimensional (1D) Galois number sequence can be folded into a two-dimensional (2D) array by the sino-representation [2]. This concept was been verified for acoustic waves a long time ago [3, 4] and is investigated here for visible light and THz waves. Our Galois diffusers are designed as reflection reliefs and realised by electron beam lithography for the optical regime and UV photolithography for the THz regime. Our results show that optical and THz Galois surfaces are excellent diffusers for electromagnetic waves; they distribute the reflected intensity evenly over a large number of maxima nearly within the entire half solid angle in the backward direction.
Optimal scanning function of a galvanometer scanner for an increased duty cycle

We study different possible scanning functions of a galvanometer-based scanner (GS), considered with an optimal profile for the user, i.e., symmetrical, linear on their active portions, and with fast stop-and-turn parts. The scope is to obtain the function that provides the highest theoretical limit of the duty cycle of the device. From the equation of the oscillatory mirror, the active torque is obtained with regard to the scanning function. Several equations are studied for the stop-and-turn parts: polynomials of different orders and sinusoidal. We demonstrate that the choice has to be done between the two most advantageous scanning functions: linear plus parabolic and linear plus sinusoidal. The relationships between the characteristic parameters of the GS, i.e., stop-and-turn time interval, scan frequency and velocity, duty cycle, and maximum inertia torque are deduced and compared for these two functions. We demonstrate that, contrary to what is considered in the literature, the best function, i.e., the one that provides the highest duty cycle (and to obtain that, the lowest inertia torque, for minimum stop-and-turn time) is the linear plus parabolic function.
Concentration and composition measurement of sprays with a global rainbow technique

Applications of the global rainbow technique to measure the concentration of a sprayed bi-component solution or the composition and the relative proportions of two sprays of different solutions at a local point are investigated. For a dual spray, the global rainbow signal is processed by optimally fitting the initial global rainbow signal with two global rainbow signals. For each composition, the value of the refractive index and size distribution is measured. In the case of spray mixing, the relative proportion of each composition can be retrieved from this information. The algorithms are validated by the processing of simulated global rainbows. Experimental rainbows of water–ethanol solutions with volume concentration from 0% to 100% and two sprays of water and ethanol are measured. The limitations of the global rainbow technique for concentration and composition measurements are discussed.
Hysteresis in gadolinium oxide metal-oxide-semiconductor capacitors

This investigation explores how oxidation affects the gadolinium oxide capacitor and the corresponding Gd2O3 hysteresis phenomenon. The current-voltage varied with Gd2O3 thickness and a charged capacitance voltage (C-V) curve with a left shift is also observed in experimental results. The breakdown voltages rise with increasing oxidation time, while the corresponding C-V hysteresis gaps decrease with increasing oxidation time.
Nucleation and growth of geological faults

We present a new model of fault nucleation and growth based on the Weibull theory, already widely used in fracture research engineering. We propose that, according to a birth-and-growth process, germs (nuclei) are born at random instants at random spatial locations and then grow with time. This leads to a satisfactory formulation of fault length distribution, different from classical statistical laws. Especially, this formulation reconciles previous analyses of fault datasets displaying power-law and/or exponential behaviors. The Weibull parameters can be statistically estimated in a simple way. We show that the model can be successfully fitted to natural data in Kenya and Ethiopia. In contrast to existing descriptive models developed for geological fault systems, such as fractal approaches, the Weibull theory allows to characterize the strength of the material, i.e. its resistance to deformation. Since this model is very general, we expect that it can be applied in many situations, and for simulations of geological fracture processes. The model is independent of deformation intensity and type and therefore allows a better constraint of the seismic risk in threatened regions.
A study of the polarization properties of a rectangular polarization-maintaining fiber

An experimental study of the polarization properties of a rectangular polarization-maintaining (PM) fiber is reported. The extinction ratio is measured at 1320 nm for lengths of 20, 40, 100, 310, and 804 m. The length dependence of the extinction ratio is demonstrated to be in excellent agreement with the predictions of a model derived from random coupling theory. This agreement is due to the careful winding and packaging of the rectangular PM fiber, whereby minimal external perturbations are introduced. This is facilitated by the rectangular geometry of the fiber. The polarization-holding parameter is 2.08+or-0.09*10/sup -6/ m/sup -1/. >
Numerical simulation of complex flows of non‐Newtonian fluids using the stream tube method and memory integral constitutive equations

In this paper a memory integral viscoelastic equation is considered for simulating complex flows of non-Newtonian fluids by stream tube analysis. A formalism is developed to take into account co-deformational memory equations in a mapped computational domain where the transformed streamlines are parallel and straight. The particle-tracking problem is avoided. Evolution in time and related kinematic quantities involved with a K-BKZ integral constitutive model are easily taken into account in evaluating the stresses. Successive subdomains, the stream tubes, may be considered for computing the main flow in abrupt axisymmetric contractions from the wall to the central flow region. The ‘peripheral stream tube’ close to the duct wall is determined by developing a non-conventional modified Hermite element. A mixed formulation is adopted and the relevant non-linear equations are solved numerically by the Levenberg-Marquardt algorithm. Although the singularity at the section of contraction is not involved explicitly, the results obtained for the peripheral stream tube clearly show the singularity effects and the extent of the recirculating zone near the salient corner. The algorithm is stable even at high flow rates and provides satisfactory solutions when compared with similar calculations in the literature.
Source of 1∕f noise in carbon nanotube devices

We report the observation of 1∕f noise in carbon nanotube field-effect transistors as a function of gate potential. In order to avoid additional low frequency noise contribution from SiO2-nanotube interactions, the measurements were carried out in conducting liquid. Based on the experimental results, we conclude that the number of carrier fluctuations is the source of the 1∕f noise.
Bayesian inference in physics: case studies

This report describes the Bayesian approach to probability theory with emphasis on the application to the evaluation of experimental data. A brief summary of Bayesian principles is given, with a discussion of concepts, terminology and pitfalls. The step from Bayesian principles to data processing involves major numerical efforts. We address the presently employed procedures of numerical integration, which are mainly based on the Monte Carlo method. The case studies include examples from electron spectroscopies, plasma physics, ion beam analysis and mass spectrometry. Bayesian solutions to the ubiquitous problem of spectrum restoration are presented and advantages and limitations are discussed. Parameter estimation within the Bayesian framework is shown to allow for the incorporation of expert knowledge which in turn allows the treatment of under-determined problems which are inaccessible by the traditional maximum likelihood method. A unique and extremely valuable feature of Bayesian theory is the model comparison option. Bayesian model comparison rests on Ockham's razor which limits the complexity of a model to the amount necessary to explain the data without fitting noise. Finally we deal with the treatment of inconsistent data. They arise frequently in experimental work either from incorrect estimation of the errors associated with a measurement or alternatively from distortions of the measurement signal by some unrecognized spurious source. Bayesian data analysis sometimes meets with spectacular success. However, the approach cannot do wonders, but it does result in optimal robust inferences on the basis of all available and explicitly declared information.
Novel Use of Thermoplastic Plates for Rewritable Holographic Stereograms

Thermoplastic (TP) plates have been used as a recording material for rewritable holographic stereograms (HSs). This novel application has made it possible to automatically control the entire process of HS synthesis without chemical development, showing that the developed HSs have high potential for 3-D display. The HS has been recorded on a TP plate of 76×76 mm2 in 130 s, using a liquid crystal television panel as a spatial light modulator for ray-traced 30-frame images. The TP plate has the capability for multiple use (more than 20 times) for HS synthesis. A reconstructed image has been observed with a good stereoscopic effect by means of He-Ne laser illumination.
Identification of Salt Tolerance Related Candidate Genes in ‘Sea Rice 86’ at the Seedling and Reproductive Stages Using QTL-Seq and BSA-Seq

Salt stress seriously affects plant growth and development and reduces the yield of rice. Therefore, the development of salt-tolerant high-yielding rice cultivars through quantitative trait locus (QTL) identification and bulked segregant analysis (BSA) is the main focus of molecular breeding projects. In this study, sea rice (SR86) showed greater salt tolerance than conventional rice. Under salt stress, the cell membrane and chlorophyll were more stable and the antioxidant enzyme activity was higher in SR86 than in conventional rice. Thirty extremely salt-tolerant plants and thirty extremely salt-sensitive plants were selected from the F2 progenies of SR86 × Nipponbare (Nip) and SR86 × 9311 crosses during the whole vegetative and reproductive growth period and mixed bulks were generated. Eleven salt tolerance related candidate genes were located using QTL-seq together with BSA. Real time quantitative PCR (RT-qPCR) analysis showed that LOC_Os04g03320. 1 and BGIOSGA019540 were expressed at higher levels in the SR86 plants than in Nip and 9311 plants, suggesting that these genes are critical for the salt tolerance of SR86. The QTLs identified using this method could be effectively utilized in future salt tolerance breeding programs, providing important theoretical significance and application value for rice salt tolerance breeding.
Research on Risk Assessment and Contamination Monitoring of Potential Toxic Elements in Mining Soils

Potentially toxic element (PTE) contamination in soils has serious impacts on ecosystems. However, there is no consensus in the field of assessment and monitoring of contaminated sites in China. In this paper, a risk assessment and pollution monitoring method for PTEs was proposed and applied to a mining site containing As, Cd, Sb, Pb, Hg, Ni, Cr, V, Zn, Tl, and Cu. The comprehensive scoring method and analytical hierarchical process were used to screen the priority PTEs for monitoring. The potential ecological risk index method was used to calculate the risk index of monitoring point. The spatial distribution characteristics were determined using semi-variance analysis. The spatial distribution of PTEs was predicted using ordinary kriging (OK) and radial basis function (RBF). The results showed that the spatial distribution of As, Pd, and Cd are mainly influenced by natural factors, while Sb and RI are influenced by both natural and human factors. OK has higher spatial prediction accuracy for Sb and Pb, and RBF has higher prediction accuracy for As, Cd, and RI. The areas with high ecological risk and above are mainly distributed on both sides of the creek and road. The optimized long-term monitoring sites can achieve the monitoring of multiple PTEs.
High Latitude Ionospheric Gradient Observation Results from a Multi-Scale Network

In this article, a cluster comprised of eight Continuously Operating Reference Station (CORS) receivers surrounding five supplemental test stations located on much shorter baselines is used to form a composite multi-scale network for the purpose of isolating, extracting, and analyzing ionospheric spatial gradient phenomena. The purpose of this investigation is to characterize the levels of spatial decorrelation between the stations in the cluster during the periods with increased ionospheric activity. The location of the selected receiver cluster is at the auroral zone at night-time (cluster centered at about 69.5° N, 19° E) known to frequently have increased ionospheric activity and observe smaller size of high-density irregularities. As typical CORS networks are relatively sparse, there is a possibility that spatially small-scale ionospheric delay gradients might not be observed by the network/closest receiver cluster but might affect the user, resulting in residual errors affecting system accuracy and integrity. The article presents high level statistical observations based on several hundred manually validated ionospheric spatial gradient events along with low level analysis of specific events with notable temporal/spatial characteristics.
Synthesis of Orbital Angular Momentum Antennas for Target Localization

The exploitation of Orbital Angular Momentum (OAM) is proposed to design circular‐support continuous aperture distributions and concentric‐ring arrays for target‐localization applications. The proposed approach allows generating a single isolated null of the field in the target direction, which is of interest with respect to common techniques leading to a line (or perpendicular lines) of power‐pattern zeroes in the spectral plane. In the presented design procedure, the optimal continuous aperture source is first identified by resorting to the most suitable OAM vortex and effective tools such as the Singular Value Decomposition of the involved operator and a smart Convex Programming optimization. Then, by exploiting fast deterministic algorithms, this continuous source is discretized into circular‐ring arrays emulating it at best. Relevant applications including equispaced arrays, sum‐difference reconfigurable arrays, and isophoric sparse arrays are presented. The proposed approach is also compared with the state‐of‐the‐art techniques and assessed through full‐wave electromagnetic simulations.
A Scalable, Modular Degasser for Passive In-Line Removal of Bubbles from Biomicrofluidic Devices

Bubbles are a common cause of microfluidic malfunction, as they can perturb the fluid flow within the micro-sized features of a device. Since gas bubbles form easily within warm cell culture reagents, degassing is often necessary for biomicrofluidic systems. However, fabrication of a microscale degasser that can be used modularly with pre-existing chips may be cumbersome or challenging, especially for labs not equipped for traditional microfabrication, and current commercial options can be expensive. Here, we address the need for an affordable, accessible bubble trap that can be used in-line for continuous perfusion of organs-on-chip and other microfluidic cultures. We converted a previously described, manually fabricated PDMS degasser to allow scaled up, reproducible manufacturing by commercial machining or fused deposition modeling (FDM) 3D printing. After optimization, the machined and 3D printed degassers were found to be stable for >2 weeks under constant perfusion, without leaks. With a ~140 µL chamber volume, trapping capacity was extrapolated to allow for ~5–20 weeks of degassing depending on the rate of bubble formation. The degassers were biocompatible for use with cell culture, and they successfully prevented bubbles from reaching a downstream microfluidic device. Both degasser materials showed little to no leaching. The machined degasser did not absorb reagents, while the FDM printed degasser absorbed a small amount, and both maintained fluidic integrity from 1 µL/min to >1 mL/min of pressure-driven flow. Thus, these degassers can be fabricated in bulk and allow for long-term, efficient bubble removal in a simple microfluidic perfusion set-up.
Does farm size matter for participation in a land fallowing policy? Evidence from China

ABSTRACT Land fallowing policy reduces the negative resource-use externalities, including water resources. Previous studies of land fallowing policies identified different factors that explain the willingness of farmers to participate in these programmes. However, less attention was placed on farm size as an important explanatory variable. We develop a theoretical model to explain the role of farm size in decisions to participate in land fallowing programmes. We then apply the theory to the Seasonal Land Fallowing Policy (SLFP), enacted to reduce agricultural groundwater use by fallowing the cultivated land of winter wheat in Hebei Province, China. Both small- and large-scale farmers participate in the programme. Using survey data, we examined whether farm size matters in decisions to participate as part of a set of variables, including farm and farmer characteristics and government requirements. Our results indicate that farm size significantly affects participation in the programme— the larger the farm, the more likely it will participate. The results are robust to various specifications. We also find that government requirements largely impact the decisions of small-scale farmers to participate. The findings have important implications for policy formulation and distinction among small- and large- scale farms.