Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of Different Potassium Levels on the Growth and Development of Cordyline fruticosa Var ‘Purple Compacta’

Original Research Article An assessment under shade house condition was conducted to identify the effects of graded potassium levels on vegetative growth of cordyline (Cordyline fruticosa var. ‘purple compacta’) plants in the Eastern region, Sri Lanka for a period of 4 months from January to April 2019 and investigation was a completely randomized design with twenty replications per treatment. Six treatments included 0.0 (Control), 0.5, 1.0, 1.5, 2.0 and 2.5 g of potassium/plant/month (g/p/m). Muriate of potash was used as a potassium source. Nitrogen (Urea) and Phosphorous (TSP) were applied at the recommended and fixed rates of 0.5 g/plant/month and 0.5g/plant/month respectively. Recommended agronomic practices were followed uniformly for all treatments. Growth parameters viz. plant height, leaf area, plant biomass and leaf nitrogen content (SPAD) were measured at monthly interval. Analysis of Variance was performed to determine significant difference among treatments (p < 0.05). Results revealed that plants applied with T2 showed significantly (p < 0.05) better performance than the other treatments in the measured growth parameters viz. plant height, leaf area, plant biomass and leaf nitrogen content. The results indicated that potassium level of 0.5 g/p/m could be the most appropriate to maximize the growth of cordyline (Cordyline fruticosa var. ‘purple compacta’) among the treatments used in this investigation under 50% shade condition.
Slab Dehydration and Mantle Upwelling in the Vicinity of the Sumatra Subduction Zone: Evidence from Receiver Function Imaging of Mantle Transition Zone Discontinuities

A total of 10,586 P‐to‐S radial receiver functions recorded by 64 broadband seismic stations were utilized to image the 410 and 660 km discontinuities (d410 and d660, respectively) bordering the mantle transition zone (MTZ) beneath the Sumatra Island, the Malay Peninsula, and the western margin of the South China Sea. The d410 and d660 were imaged by stacking receiver functions in successive circular bins with a radius of 1°, after moveout corrections based on the 1‐D IASP91 Earth model. The resulting apparent depths of the discontinuities exhibit significant and spatially systematic variations. The apparent depths of the d410 and d660 range from 382 to 459 km and 637 to 700 km with an average of 406 ± 13 and 670 ± 12 km, respectively, while the corresponding values for the MTZ thickness are 217 to 295 km and 261 ± 13 km. Underneath southern Sumatra and adjacent regions, the MTZ is characterized by an uplifted d410 and a depressed d660. While the former is probably caused by the low temperature anomaly, the latter is most likely related to a combination of the low temperature anomaly and dehydration associated with the subducted Australian Plate that has reached at least the d660. In contrast, an abnormally thin MTZ is imaged to the southwest of the Toba Caldera. This observation, when combined with results from previous seismic tomography studies, can be explained by advective thermal upwelling through a slab window.
DAMD 1796-1-6055 TITLE : Estrogen Effects on Breast Tumor Growth in Estrogen Receptor-Minus

We have developed a PCR-based method, called methylation-sensitive restriction fingerprinting (MSRF), to screen changes in DNA methylation in breast carcinomas. Two hypermethylation-containing fragments, HBC-1 (for "hypennethylation in breast cancer") and HBC-2, were identified in the amplified breast tumor DNA relative to the amplified normal breast DNA of a patient Nudeotide sequence analysis revealed no significant matches between the sequence of HBC-1 and the known sequences in the GenBank database, whereas the sequence of HBC-2 matched the upstream region of an antisense WT1 (WUms' tumor suppressor gene) promoter. The methylation status in the breast tumor DNA from this patient was confirmed by Southern hybridization using HBC-1 and HBC-2 as probes, respectively. Further analysis showed that HBC-1 was methylated aberrantly in 90% (17 of 19 patients) of the primary breast carcinomas examined. This study demonstrates that MSRF provides a useful means for screening aberrant changes in DNA methylation during tumorigenesis. The commonly methylated fragments identified by MSRF could potentially supplement pathological markers currently used for cancers and additionally lead to the discovery of novel methylated tumor suppressor genes.
Deterministic Min-cut in Poly-logarithmic Max-flows

We give a deterministic (global) min-cut algorithm for weighted undirected graphs that runs in time $O(m^{1+\epsilon})$ plus polylog ($n$) max-flow computations. Using the current best max-flow algorithms, this results in an overall running time of $\tilde{O}(m\cdot\min(\sqrt{m}, n^{2/3}))$ for weighted graphs, and $m^{4/3+o(1)}$ for unweighted (multi)-graphs. This is the first improvement in the running time of deterministic algorithms for the min-cut problem on general (weighted/multi) graphs since the early 1990s when a running time bound of $\tilde{O}(mn)$ was established for this problem.
Semi-parametric Item Response Functions in the Context of Guessing Authors:

We present a logistic function of a monotonic polynomial with a lower asymptote, allowing additional flexibility beyond the three-parameter logistic model. We develop a maximum marginal likelihood based approach to estimate the item parameters. The new item response model is demonstrated on math assessment data from a state, and a computationally efficient strategy for choosing the order of the polynomial is demonstrated. Finally, our approach is tested through simulations and compared to response function estimation using smoothed isotonic regression. Results indicate that our approach can result in small gains in item response function recovery and latent trait estimation.
Understanding Cloud Droplet Spectral Dispersion Effect Using Empirical and Semi‐Analytical Parameterizations in NCAR CAM5.3

Five parameterizations of cloud droplet spectral shape are implemented in a global climate model to investigate the dispersion effect and aerosol indirect effect (AIE). We design a series of experiments by modifying the microphysical cloud scheme of NCAR CAM5.3 (National Center for Atmospheric Research Community Atmosphere Model Version 5.3). We employ four empirical (Martin94, RLiu03, PengL03, and Liu08) and one semi‐analytical (LiuLi15) expressions for cloud droplet spectral shape parameters. Analysis focuses on the instantaneous differences in the simulated cloud microphysical properties and the comparison between model output and satellite data. The results show that RLiu03, PengL03, and LiuLi15 produce wider droplet spectrum and faster autoconversion rate, but Liu08 has a narrower droplet spectrum and slower autoconversion rate than the default parameterization (Martin94) in CAM5.3. Global dispersion effects caused by the five parameterizations modify the aerosol indirect effect by −10% (counteract) to 13% (strengthen). The simulated AIEs and dispersion effects exhibit noticeably spatial inhomogeneity. In the sensitive regions of AIE (Southeast Asia, North Pacific, and West Coast of South America), we decompose the response of shortwave cloud forcing to the change in droplet number for analysis. The varying dispersion effects can be explained by different responses of cloud properties in different spectral parameterizations.
Confirmatory test of gamma irradiation against the larvae and pupae of Helicoverpa assulta (Lepidoptera: Noctuidae) in paprika

The oriental tobacco budworm, Helicoverpa assulta (Lepidoptera: Noctuidae), is classified as a quarantine pest that must be controlled for the exportation of Korean paprika to the USA. We performed a confirmatory test of gamma irradiation against the last-instar larvae and pupae of H. assulta in paprika. Previous, small-scale gamma irradiation at a dose of 100 Gy to the last-instar larvae allowed emergence of adults, though they were abnormal, but 200 Gy prevented adult emergence completely. For pupae, irradiation of 5- to 6-day-old pupae with 300 Gy prevented normal emergence completely. To gather confirmatory data applicable to phytosanitary quarantine regulations, larvae and pupae were placed inside paprika in a box and were irradiated with 200 Gy and 300 Gy, respectively. After irradiation with 200 Gy (measured doses 170-199 Gy) of 2,186 individuals of the last-instar larvae, 10.84% survived, but either formed abnormal pupae (7.57%) or died during the pupal stage (3.27%), resulting in no emergence of normal adults. For pupae, the dose of 300 Gy (measured doses 276-319 Gy) given to 1,200 pupae allowed 10.75% to survive and 9.17% to emerge with deformity. However, 1.58% of irradiated pupae emerged normally, requiring an increased dose for complete prevention of normal emergence. Subsequently, an increased dose of 400 Gy (measured doses 340-402 Gy) to 1,005 pupae allowed 88.35% to emerge, but all emerged with deformity. Thus, irradiation treatment with a minimum dose of 400 Gy will provide quarantine security for all premature H. assulta in exported paprika.
Beauty and Job Accessibility : New Evidence from a Field Experiment

This study uses a field experiment to resolve the difficulties of quantifying personal appearance and identify a direct causal relationship between appearance and employment in China. The experiment reveals that taste-based pure appearance discrimination exists at the pre-interview stage. There are significant gender-specific heterogeneous effects of education on appearance discrimination: having better educational credentials reduces appearance discrimination among men but not among women. Moreover, attributes of the labor market, companies, and vacancies matter. Beauty premiums are larger in big cities with higher concentrations of women and in male-focused research positions. Similarly, the beauty premium is larger for vacancies with higher remuneration.
High Dynamic Range Image Reconstruction from Multiple-Exposure Image )

We propose a novel HDR imaging method using a coded electronic shutter that allows capturing spatially varying exposures simultaneously in a single image. Previous HDR imaging approaches have difficulties in capturing moving objects and handling camera shakes, since they assume the target scenes to be static. Special imaging devices such as exposure-filtering masks could reduce motion artifacts, but manufacturing costs have limited their practicality. Using the proposed framework, we extend the dynamic range of an input image without extracting the subimages for different exposures while preventing jaggy artifacts and preserving fine details. Experimental results on real images captured using a prototype camera equipped with a coded electronic shutter show that our method can generate higher quality HDR images than previous approaches
Model Predictive Control for Automated Vehicle Steering

: The autonomous vehicle steering system, a multi-input multi-output (MIMO) system, is challenging to design using traditional controllers due to the interaction between inputs and outputs. If PID controllers are used the control loops are executed independently of each other as there is no interaction between the loops. Designing a larger system increases the controller parameters requiring tuning. Model Predictive Control (MPC) overcomes this problem, as it is a multi-variable control method taking into account the interactions of the variables in the target system. Achieving a high safety level is also critical for autonomous vehicle systems. This can be provided by an MPC controller, which can handle constraints such as maintaining a safe distance from other cars. Wider applicability of the Model Predictive Controller calls for more efficient hardware architectures for implementation. The aim of this paper is to achieve optimal implementation of the MPC controller by increasing the computational speed in order to reduce execution time for optimization. An MPC controller is used to control the steering system of an autonomous vehicle to keep it on the desired path. A traditional MPC controller is used to control the system where the plant dynamics do not change, whereas an Adaptive MPC controller is used when the system is nonlinear or its characteristics vary with time (the longitudinal velocity changes as the vehicle moves). Results are discussed in terms of performance, resource utilization, cost, and energy-effective implementations taking into consideration a reasonable size number of constraints handled by the controller.
Learning sampling in financial statement audits using vector quantised variational autoencoder neural networks

The audit of financial statements is designed to collect reasonable assurance that an issued statement is free from material misstatement ('true and fair presentation'). International audit standards require the assessment of a statements' underlying accounting relevant transactions referred to as 'journal entries' to detect potential misstatements. To efficiently audit the increasing quantities of such journal entries, auditors regularly conduct an 'audit sampling' i.e. a sample-based assessment of a subset of these journal entries. However, the task of audit sampling is often conducted early in the overall audit process, where the auditor might not be aware of all generative factors and their dynamics that resulted in the journal entries in-scope of the audit. To overcome this challenge, we propose the use of a Vector Quantised-Variational Autoencoder (VQ-VAE) neural networks to learn a representation of journal entries able to provide a comprehensive 'audit sampling' to the auditor. We demonstrate, based on two real-world city payment datasets, that such artificial neural networks are capable of learning a quantised representation of accounting data. We show that the learned quantisation uncovers (i) the latent factors of variation and (ii) can be utilised as a highly representative audit sample in financial statement audits.
Description, Taphonomy, and Paleoecology of the Late Pleistocene Peccaries (Artiodactyla: Tayassuidae) from Bat Cave, Pulaski County, Missouri

2 ACKNOWLEDGMENTS . 4 LIST OF TABLES . 7 LIST OF FIGURES . 9
Analytical Modeling of Nonlinear Fiber Propagation for Four Dimensional Symmetric Constellations

Coherent optical transmission systems naturally lead to a four dimensional (4D) signal space, i.e., two polarizations each with two quadratures. In this paper we derive an analytical model to quantify the impact of Kerr nonlinearity on such 4D spaces, taking the interpolarization dependency into account. This is in contrast to previous models such as the GN and EGN models, which are valid for polarization multiplexed (PM) formats, where the two polarizations are seen as independent channels on which data is multiplexed. The proposed model agrees with the EGN model in the special case of independent two-dimensional modulation in each polarization. The model accounts for the predominant nonlinear terms in a WDM system, namely self-phase modulation and and cross-phase modulation. Numerical results show that the EGN model may inaccurately estimate the nonlinear interference of 4D formats. This nonlinear interference discrepancy between the results of the proposed model and the EGN model could be up to 2.8 dB for a system with 80 WDM channels. The derived model is validated by split-step Fourier simulations, and it is shown to follow simulations very closely.
A Low-Level Index for Distributed Logic Programming

A distributed logic programming language with support for meta-programming and stream processing offers a variety of interesting research problems, such as: How can a versatile and stable data structure for the indexing of a large number of expressions be implemented with simple low-level data structures? Can low-level programming help to reduce the number of occur checks in Robinson's unification algorithm? This article gives the answers.
Usage Patterns of Privacy-Enhancing Technologies

The steady reports of privacy invasions online paints a picture of the Internet growing into a more dangerous place. This is supported by reports of the potential scale for online harms facilitated by the mass deployment of online technology and by the data-intensive web. While Internet users often express concern about privacy, some report taking actions to protect their privacy online. We investigate the methods and technologies that individuals employ to protect their privacy online. We conduct two studies, of N=180 and N=907, to elicit individuals' use of privacy methods, within the US, the UK and Germany. We find that non-technology methods are among the most used methods in the three countries. We identify distinct groupings of privacy methods usage in a cluster map. The map shows that together with non-technology methods of privacy protection, simple PETs that are integrated in services, form the most used cluster, whereas more advanced PETs form a different, least used cluster. We further investigate user perception and reasoning for mostly using one set of PETs, in a third study with N=183 participants. We do not find a difference in perceived competency in protecting privacy online between advanced and simpler PETs users. We compare use perceptions between advanced and simpler PETs and report on user reasoning for not using advanced PETs, as well as support needed for potential use. This paper contributes to privacy research by eliciting use and perception of use across 43 privacy methods, including 26 PETs across three countries and provides a map of PETs usage. The cluster map provides a systematic and reliable point of reference for future user-centric investigations across PETs. Overall, this research provides a broad understanding of use and perceptions across a collection of PETs, and can lead to future research for scaling use of PETs.
SSMBA: Self-Supervised Manifold Based Data Augmentation for Improving Out-of-Domain Robustness

Models that perform well on a training domain often fail to generalize to out-of-domain (OOD) examples. Data augmentation is a common method used to prevent overfitting and improve OOD generalization. However, in natural language, it is difficult to generate new examples that stay on the underlying data manifold. We introduce SSMBA, a data augmentation method for generating synthetic training examples by using a pair of corruption and reconstruction functions to move randomly on a data manifold. We investigate the use of SSMBA in the natural language domain, leveraging the manifold assumption to reconstruct corrupted text with masked language models. In experiments on robustness benchmarks across 3 tasks and 9 datasets, SSMBA consistently outperforms existing data augmentation methods and baseline models on both in-domain and OOD data, achieving gains of 0.8% accuracy on OOD Amazon reviews, 1.8% accuracy on OOD MNLI, and 1.4 BLEU on in-domain IWSLT14 German-English.
Event Rates for the Scattering of Weakly Interacting Massive Particles from 23Na and 40Ar

Detection rates for the elastic and inelastic scattering of weakly interacting massive particles (WIMPs) off 23Na are calculated within the framework of Deformed Shell Model (DSM) based on Hartree-Fock states. At first, the spectroscopic properties of the detector nucleus, like energy spectra and magnetic moments, are evaluated and compared with experimental data. Following the good agreement of these results, DSM wave functions are used for obtaining elastic and inelastic spin structure functions, nuclear structure coefficients and so forth for the WIMP-23Na scattering. Then, the event rates are also computed with a given set of supersymmetric parameters. In the same manner, using DSM wavefunctions, nuclear structure coefficients and event rates for elastic scattering of WIMPs from 40Ar are also obtained. These results for event rates and also for annual modulation will be useful for the ongoing and future WIMP detection experiments involving detector materials with 23Na and 40Ar nuclei.
Optical Hall response of bilayer graphene: Manifestation of chiral hybridized states in broken mirror symmetry lattices

Understanding mechanisms governing the optical activity of layered-stacked materials is crucial for designing devices aimed to manipulate light at the nanoscale. Here, we show that both the twisted and slid bilayer graphene are chiral systems able to deflect the polarization of the linear polarized light. However, only the twisted bilayer graphene supports the circular dichroism. Our calculation scheme, based on the time-dependent Schrodinger equation, is specifically efficient for calculating the optical-conductivity tensor. In particular, it allows us showing the chirality of hybridized states as the handedness-dependent bending of the trajectory of kicked Gaussian wave packets in the bilayer lattices. We show that the nonzero Hall conductivity is the result of the non-cancelling manifestation of hybridized states in chiral lattices. We also demonstrate the continuous dependence of the conductivity tensor on the twist angle and the sliding vector.
Segmentation and Defect Classification of the Power Line Insulators: A Deep Learning-based Approach

Power transmission networks physically connect the power generators to the electric consumers. Such systems extend over hundreds of kilometers. There are many components in the transmission infrastructure that require a proper inspection to guarantee flawless performance and reliable delivery, which, if done manually, can be very costly and time consuming. One essential component is the insulator. Its failure can cause an interruption of the entire transmission line or a widespread power failure. Automated fault detection could significantly decrease inspection time and related costs. Recently, several works have been proposed based on convolutional neural networks, which address the issue mentioned above. However, existing studies focus on a specific type of insulator faults. Thus, in this study, we introduce a two-stage model that segments insulators from their background to then classify their states based on four different categories, namely: healthy, broken, burned/corroded and missing cap. The test results show that the proposed approach can realize the effective segmentation of insulators and achieve high accuracy in detecting several tynes of faults.
Forward modelling of particle acceleration and transport in an individual solar flare

The aim of this study is to generate maps of the hard X-ray emission produced by energetic electrons in a solar flare and compare them with observations. The ultimate goal is to test the viability of the combined MHD/test-particle approach for data-driven modelling of active events in the solar corona and their impact on the heliosphere. Based on an MHD model of X-class solar flare observed on the 8th of September 2017, we calculate trajectories of a large number of electrons and protons using the relativistic guiding-centre approach. Using the obtained particle trajectories, we deduce the spatial and energy distributions of energetic electrons and protons, and calculate bremsstrahlung hard X-ray emission using the 'thin target' approximation. Our approach predicts some key characteristics of energetic particles in the considered flare, including the size and location of the acceleration region, energetic particle trajectories and energy spectra. Most importantly, the hard X-ray bremsstrahlung intensity maps predicted by the model are in a good agreement with those observed by RHESSI. Furthermore, the locations of proton and electron precipitation appear to be close to the sources of helioseismic response detected in this flare. Therefore, the adopted approach can be used for observationally-driven modelling of individual solar flares, including manifestations of energetic particles in the corona, as well as inner heliosphere.