Miracle-dz/UniFAR-FTUs huggingface dataset:
Validation of SATURN, a free, electronic, self‐administered cognitive screening test

Abstract Background Cognitive screening is limited by clinician time and variability in administration and scoring. We therefore developed Self‐Administered Tasks Uncovering Risk of Neurodegeneration (SATURN), a free, public‐domain, self‐administered, and automatically scored cognitive screening test, and validated it on inexpensive (<$100) computer tablets. Methods SATURN is a 30‐point test including orientation, word recall, and math items adapted from the Saint Louis University Mental Status test, modified versions of the Stroop and Trails tasks, and other assessments of visuospatial function and memory. English‐speaking neurology clinic patients and their partners 50 to 89 years of age were given SATURN, the Montreal Cognitive Assessment (MoCA), and a brief survey about test preferences. For patients recruited from dementia clinics (n = 23), clinical status was quantified with the Clinical Dementia Rating (CDR) scale. Care partners (n = 37) were assigned CDR = 0. Results SATURN and MoCA scores were highly correlated (P < .00001; r = 0.90). CDR sum‐of‐boxes scores were well‐correlated with both tests (P < .00001) (r = −0.83 and −0.86, respectively). Statistically, neither test was superior. Most participants (83%) reported that SATURN was easy to use, and most either preferred SATURN over the MoCA (47%) or had no preference (32%). Discussion Performance on SATURN—a fully self‐administered and freely available (https://doi.org/10.5061/dryad.02v6wwpzr) cognitive screening test—is well‐correlated with MoCA and CDR scores.
EVALUATION OF AMMONIUM RELEASE BY ALKALINE PERMANGANATE EXTRACTION AS AN INDEX OF SOIL NITROGEN AVAILABILITY

The relation of potentially mineralizable soil nitrogen, No to hydrolytic and oxidative release of NH4-N from soil organic matter by extraction with alkaline permanganate was investigated using 62 soils representing several major agricultural regions in the United States. The NH4-N was recovered by steam-distilling 1-g samples of soil during extraction of 4 min with several concentrations of NaOH and KMnO4 in different combinations. The same concentrations of NaOH and KMnO4, were used to determine amounts of NH4-N released by hydrolysis (B) during steam distillation. Oxidative release was estimated as the difference between total NH4-N produced during alkaline permanganate extraction (BOx) and that derived by NaOH distillation. These NH4-N fractions were determined using inital NaOH concentrations ranging from 0.25 to 0.625 N, alone or with amounts of KMnO4 ranging from 0.032 to 0.5 g/10 ml of extractant. For all combinations of NaOH and KMnO4, the correlation coefficients relating B and BOx consistently were high (r = > 0.9). Correlations between the independent variables B and (BOx - B), ranged from r = 0.17 to 0.90. Multiple regressions of No on B and BOx were associated with coefficients of multiple correlation, R, that were relatively constant (generally, 0.8 to 0.9), despite the diversity of reagent concentrations used for extraction. THese and other considerations indicate that the NH4-N released by NaOH hydrolysis (B) and by KMnO4 oxidation (BOx - B) was derived, in large part, from the same soil organic sources (e.g., amides, amines, amino acids, and amino sugars). With few exceptions, correlations of No with BOx were as good as, or better than, simple correlations of No with B or (BOx - B). Thus, the extra effort required for separate NaOH and NaOH-KMnO4 extractions would not appear to be justified. The alkaline permanganate methods thus far reported by various investigators, as well as modifications evaluated in this study, offer a less precise and reliable basis for predicting No than does acid permanganate extraction for measuring oxidative release of NH4-N.
Impact of soil texture on temporal and spatial development of osmotic-potential gradients between bulk soil and rhizosphere

Solute transport from the bulk soil to the root surface is, apart from changes in soil moisture and plant nutrient uptake, a prerequisite for changes in soil osmotic potential (Ψ 0 ). According to the convection-diffusion equation, solute transport depends on a number of parameters (soil moisture-release curve, hydraulic conductivity, tortuosity factor) which are functions of soil texture. It was thus hypothesized that soil texture should have an effect on the formation of Ψ 0 gradients between bulk soil and the root surface. The knowledge about such gradients is important to evaluate water availability in the soil-plant-atmosphere continuum (SPAC). A linear compartment system with maize grown under controlled conditions in two texture treatments (T1, pure sand; T2, 80% sand, 20% silt) under low and high initial application of salts (S1, S2) was used to measure the development of Ψ 0 gradients between bulk soil and the root surface by microscale soil-solution sampling and TDR sensors. The differences in soil texture had a strong impact on the formation of Ψ 0 gradients between bulk soil and the root surface at high and low initial salt application rate. At high initial salt application, a maximum osmotic-potential gradient (ΔΨ 0 ) of -340 kPa was observed for the texture treatment T2 compared to ΔΨ 0 of -180 in T1. The steeper gradients in osmotic potential in treatment T2 compared to T1 corresponded to higher cumulative water consumption in this treatment which can partly be explained by higher soil hydraulic conductivity in the range of soil matric potentials covered during the duration of the experiments. Differences between texture treatments in Ψ 0 at the root surface did not result in differences in plant-water relations measured as gas-exchange parameters (transpiration rate, water-use efficiency) and leaf osmotic potential. If soil osmotic and matric potential are regarded as additive in calculating the driving force for water movement from the soil into the root, the observed differences in water flux between treatments cannot be explained.
The Preparation of TiO2 Nanocrystal by Repetitious Hydrothermal Method and Its Application in Flexible Dye-sensitized Solar Cells

Abstract TiO2 nanocrystal is prepared by a repetitious hydrothermal crystallization method. Based on the TiO2 nanocrystal, a flexible photoanode film and flexible dye-sensitized solar cell are assembled at low temperatures. The influences of agglutinant, low temperature treatment, and UV irradiation on the photoelectric properties of the flexible cells are investigated. The dye-sensitized solar cells using FTO/glass and ITO/PEN as substrates achieve light-to-electric energy conversion efficiencies of 4.47 and 2.92%, respectively.
Exciton optical transitions in a hexagonal boron nitride single crystal

Near band gap photoluminescence (PL) of a hexagonal boron nitride single crystal has been studied at cryogenic temperatures with synchrotron radiation excitation. The PL signal is dominated by trapped‐exciton optical transitions, while the photoluminescence excitation (PLE) spectra show features assigned to free excitons. Complementary photoconductivity and PLE measurements set the band gap transition energy to 6.4 eV and the Frenkel exciton binding energy larger than 380 meV. (© 2011 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)
Studies on Neurosteroids. VII. Characterization of Pregnenolone, Its Sulfate and Dehydroepiandro-Sterone in Rat Brains Using Liquid Chromatography/Mass Spectrometry

Abstract The separation and characterization of pregnenolone, its 3-sulfate and dehydroepiandrosterone in rat brains (neurosteroids) were carried out using liquid chromatography/mass spectrometry. The genins obtained from rat brains were acetylated and then derivatized with O-methylhydroxylamine to give the respective acetate-methyloximes, which were identified by comparison of their chromatographic behavior with authentic samples during liquid chromatography/atmospheric pressure chemical ionization-mass spectrometry. Pregnenolone 3-sulfate in rat brains was also identified with an authentic sample using liquid chromatography/electrospray ionization-mass spectrometry.
Mechanistic Investigation on the Reaction of O− with CH3CN Using Density Functional Theory

The potential energy profile of the reaction between the atomic oxygen radical anion and acetonitrile has been mapped at the G3MP2B3 level of theory. Geometries of the reactants, products, intermediate complexes, and transition states involved in this reaction have been optimized at the (U)B3LYP/6-31+G(d,p) level, and then their accurate relative energies have been improved using the G3MP2B3 method. The potential energy profile is confirmed via intrinsic reaction coordinate calculations of transition states. Four possible production channels are examined respectively, as H+ transfer, H-atom transfer, H2+ transfer, and bi-molecular nucleophilic substitution (SN2) reaction pathways. Based on present calculations, the H2+ transfer reaction is major among these four channels, which agrees with previous experimental conclusions.
Chasing the Tail in Monocular 3D Human Reconstruction With Prototype Memory

Deep neural networks have achieved remarkable progress in single-image 3D human reconstruction. However, existing methods still fall short in predicting rare poses. The reason is that most of the current models perform regression based on a single human prototype, which is similar to common poses while far from the rare poses. In this work, we 1) identify and analyze this learning obstacle and 2) propose a prototype memory-augmented network, PM-Net, that effectively improves performances of predicting rare poses. The core of our framework is a memory module that learns and stores a set of 3D human prototypes capturing local distributions for either common poses or rare poses. With this formulation, the regression starts from a better initialization, which is relatively easier to converge. Extensive experiments on several widely employed datasets demonstrate the proposed framework’s effectiveness compared to other state-of-the-art methods. Notably, our approach significantly improves the models’ performances on rare poses while generating comparable results on other samples.
Correlations between SFG Spectra and Electrical Properties of Organic Field Effect Transistors

Simultaneous sum frequency generation (SFG) vibrational spectra and electrical measurements were obtained on organic field effect transistors (OFETs) fabricated with the semiconductors: 5,5‘-bis(4-hexylphenyl)-2,2‘-bithiophene (6pttp6), 5,5‘-bis(4-ethylphenyl)-2,2‘-bithiophene (2pttp2), and pentacene. In-situ measurements during gating of the OFETs showed strong correlations between vibrational spectra and electronic properties. One correlation involved structural changes in the hexyl and ethyl groups, of 6pttp6 and 2pttp2, respectively, and saturation source-drain current; the correlation was observed only at negative gate voltages (when carrier injection was possible) and was more pronounced for 6pttp6, with the introduction of gauche defects in the longer hexyl chains. A second correlation between the dependence of SF nonresonant background on gate voltage and electronic mobility was observed on OFETs of all three semiconductors, at both positive and negative gate voltages. This correlation suggests t.
Managed Information: A New Abstraction Mechanism for Handling Information in Software-as-a-Service

Management of information is an important aspect of every application. This includes, for example, protecting user data against breaches (like the one reported in the news about 50 million Facebook profiles being harvested for Cambridge Analytica), complying with data protection laws and regulations (like EU's new General Data Protection Regulation), coping with large databases, and retaining user data across software versions. Today, every application needs to cope with such concerns by itself and on its own. In this paper we introduce Managed Information (MI), an abstraction mechanism for managing extra-functional data related concerns, similar to how managed memory today abstracts away many memory related concerns. MI limits the access applications have to user data, which, in return, relieves them from responsibility over it. This is achieved by hosting them on a Managed Information Platform (MIP), and implementing their logic in a language that supports MI. As evidence for the feasibility of MI we describe the design and implementation of such a platform. For demonstration of MI, we describe a simple social network application built with it. The implementation is open source.
The Wettability of Some Concrete Powders

Adhesion properties and wettability of broken concrete debris influence not only the behavior of the material during wet screening and mixing, but also affect the hydration process of the final mixture. Contact angle measurements were performed on concrete powders and cement paste powders with the Washburn method. The estimated angles are in the range between 72° and 80°. The concentration of quartz and basalt in the mixtures does not significantly affect the contact angle. Based on this study it seems that the hardened cement paste formed during the hydration dominates the wettability of the powders. powder wettability Washburn contact angle
Heavy dark matter and gravitational waves

Domain walls can form after breakdown of a discrete symmetry induced by first-order phase transition, we study the heavy dark matter produced around the temperature of the phase transition that yields the breakdown of a $\mathbb{Z}_{3}$ symmetry. The generated gravitational waves by domain walls decay is found to be able to probed by the Pulsar Timing Arrays, and the future Square Kilometer Array.
Privacy-Preserving Methods for Vertically Partitioned Incomplete Data

Distributed health data networks that use information from multiple sources have drawn substantial interest in recent years. However, missing data are prevalent in such networks and present significant analytical challenges. The current state-of-the-art methods for handling missing data require pooling data into a central repository before analysis, which may not be possible in a distributed health data network. In this paper, we propose a privacy- preserving distributed analysis framework for handling missing data when data are vertically partitioned. In this framework, each institution with a particular data source utilizes the local private data to calculate necessary intermediate aggregated statistics, which are then shared to build a global model for handling missing data. To evaluate our proposed methods, we conduct simulation studies that clearly demonstrate that the proposed privacy- preserving methods perform as well as the methods using the pooled data and outperform several naive methods. We further illustrate the proposed methods through the analysis of a real dataset. The proposed framework for handling vertically partitioned incomplete data is substantially more privacy-preserving than methods that require pooling of the data, since no individual-level data are shared, which can lower hurdles for collaboration across multiple institutions and build stronger public trust.
Tube-enhanced Multi-stage MPC for Flexible Robust Control of Constrained Linear Systems with Additive and Parametric Uncertainties

The trade-off between optimality and complexity has been one of the most important challenges in the field of robust Model Predictive Control (MPC). To address the challenge, we propose a flexible robust MPC scheme by synergizing the multi-stage and tube-based MPC approaches. The key idea is to exploit the non-conservatism of the multi-stage MPC and the simplicity of the tube-based MPC. The proposed scheme provides two options for the user to determine the trade-off depending on the application: the choice of the robust horizon and the classification of the uncertainties. Beyond the robust horizon, the branching of the scenario-tree employed in multi-stage MPC is avoided with the help of tubes. The growth of the problem size with respect to the number of uncertainties is reduced by handling \emph{small} uncertainties via an invariant tube that can be computed offline. This results in linear growth of the problem size beyond the robust horizon and no growth of the problem size concerning small magnitude uncertainties. The proposed approach helps to achieve a desired trade-off between optimality and complexity compared to existing robust MPC approaches. We show that the proposed approach is robustly asymptotically stable. Its advantages are demonstrated for a CSTR example.
Joint Air Quality and Weather Prediction Based on Multi-Adversarial Spatiotemporal Networks

Accurate and timely air quality and weather predictions are of great importance to urban governance and human livelihood. Though many efforts have been made for air quality or weather prediction, most of them simply employ one another as feature input, which ignores the inner-connection between two predictive tasks. On the one hand, the accurate prediction of one task can help improve another task's performance. On the other hand, geospatially distributed air quality and weather monitoring stations provide additional hints for city-wide spatiotemporal dependency modeling. Inspired by the above two insights, in this paper, we propose the Multi-adversarial spatiotemporal recurrent Graph Neural Networks (MasterGNN) for joint air quality and weather prediction. Specifically, we first propose a heterogeneous recurrent graph neural network to model the spatiotemporal autocorrelation among air quality and weather monitoring stations. Then, we develop a multi-adversarial graph learning framework to against observation noise propagation introduced by spatiotemporal modeling. Moreover, we introduce an adaptive training strategy by formulating multi-adversarial learning as a multi-task learning problem. Finally, extensive experiments on two real-world datasets show that MasterGNN achieves the best performance compared with seven baselines on both air quality and weather prediction tasks.
Detecting Anomalous line-items by Modeling the Legal Case Lifecycle

Anomaly detection continues to be the subject of research and development eﬀorts due to its wide-ranging applications and the value of detecting anomalous patterns in a variety of use cases. While many anomalies may be relatively benign, others may be more severe have the potential to signiﬁcantly impact to the business, user, or other involved parties. In ﬁnance, detecting anomalous transactions can provide dramatic improvements to ﬁnancial audits, given that many audits continue to involve signiﬁcant human eﬀort in review of accounting documents. In the case of the legal industry - which is the focus of this work - detecting anomalies is important to both data and legal integrity, and serves a secondary function in supporting downstream analytics and machine-learning capabilities. In this work, we detail an approach for detecting anomalous activities based their suitability in the legal case’s lifecycle (modeled using a set of case-level and invoice line-item-level features). We illustrate our approach for invoice line-item anomaly detection which works in the absence of labeled data, by utilizing a combination of subject matter expertise and synthetic data generation for model training. We characterize the method’s performance using a set of well understood, easily accessible model architectures. We demonstrate how this process provides a path towards solving certain anomaly detection problems when the characteristics of the anomalies are well known, and oﬀer lessons learned from applying our approach to detecting potentially anomalous line-items on real-world data.
The Parallel Meaning Bank: A Framework for Semantically Annotating Multiple Languages

This paper gives a general description of the ideas behind the Parallel Meaning Bank, a framework with the aim to provide an easy way to annotate compositional semantics for texts written in languages other than English. The annotation procedure is semi-automatic, and comprises seven layers of linguistic information: segmentation, symbolisation, semantic tagging, word sense disambiguation, syntactic structure, thematic role labelling, and co-reference. New languages can be added to the meaning bank as long as the documents are based on translations from English, but also introduce new interesting challenges on the linguistics assumptions underlying the Parallel Meaning Bank.
Scalable hyperfine qubit state detection via electron shelving in the 
2D5/2
 and 
2F7/2
 manifolds in 
171Yb+

Qubits encoded in hyperfine states of trapped ions are ideal for quantum computation given their long lifetimes and low sensitivity to magnetic fields, yet they suffer from off-resonant scattering during detection often limiting their measurement fidelity. In ${}^{171}$Yb$^{+}$ this is exacerbated by a low fluorescence yield, which leads to a need for complex and expensive hardware - a problematic bottleneck especially when scaling up the number of qubits. We demonstrate a detection routine based on electron shelving to address this issue in ${}^{171}$Yb$^{+}$ and achieve a 5.6$\times$ reduction in single-ion detection error on an avalanche photodiode to $1.8(2)\times10^{-3}$ in a 100 $\mu$s detection period, and a 4.3$\times$ error reduction on an electron multiplying CCD camera, with $7.7(2)\times10^{-3}$ error in 400 $\mu$s. We further improve the characterization of a repump transition at 760 nm to enable a more rapid reset of the auxiliary $^2$F$_{7/2}$ states populated after shelving. Finally, we examine the detection fidelity limit using the long-lived $^2$F$_{7/2}$ state, achieving a further 300$\times$ and 12$\times$ reduction in error to $6(7)\times10^{-6}$ and $6.3(3)\times10^{-4}$ in 1 ms on the respective detectors. While shelving-rate limited in our setup, we suggest various techniques to realize this detection method at speeds compatible with quantum information processing, providing a pathway to ultra-high fidelity detection in ${}^{171}$Yb$^{+}$.
Simplifying Impact Prediction for Scientific Articles

Estimating the expected impact of an article is valuable for various applications (e.g., article/cooperator recommendation). Most existing approaches attempt to predict the exact number of citations each article will receive in the near future, however this is a difficult regression analysis problem. Moreover, most approaches rely on the existence of rich metadata for each article, a requirement that cannot be adequately fulfilled for a large number of them. In this work, we take advantage of the fact that solving a simpler machine learning problem, that of classifying articles based on their expected impact, is adequate for many real world applications and we propose a simplified model that can be trained using minimal article metadata. Finally, we examine various configurations of this model and evaluate their effectiveness in solving the aforementioned classification problem.
DESIGN KNOWLEDGE REPRESENTATION WITH TECHNOLOGY SEMANTIC NETWORK

Abstract Engineers often need to discover and learn designs from unfamiliar domains for inspiration or other particular uses. However, the complexity of the technical design descriptions and the unfamiliarity to the domain make it hard for engineers to comprehend the function, behavior, and structure of a design. To help engineers quickly understand a complex technical design description new to them, one approach is to represent it as a network graph of the design-related entities and their relations as an abstract summary of the design. While graph or network visualizations are widely adopted in the engineering design literature, the challenge remains in retrieving the design entities and deriving their relations. In this paper, we propose a network mapping method that is powered by Technology Semantic Network (TechNet). Through a case study, we showcase how TechNet’s unique characteristic of being trained on a large technology-related data source advantages itself over common-sense knowledge bases, such as WordNet and ConceptNet, for design knowledge representation.