Miracle-dz/UniFAR-FTUs huggingface dataset:
Chemical Property Calculation through JavaScript and Applications in QSAR

Hanqing WuDepartment of Chemistry, University of Wisconsin-Milwaukee, Milwaukee, WI 53201, USA E-mail hanqing@csd.uwm. edu* This paper was presented at ecsoc-1 at URL of http://www.unibas. ch/mdpi/ecsoc/e0002/e0002.htmPart of this work was performed in the Department of Applied Chemistry, University of Science andTechnology of China, Hefei, Anhui, P. R. China (1984-90)Editor's note: http://www.unibas. ch/mdpi/ecsoc/e0002/e0002.htm file will be included in the CD-ROM edition.Received: 30 January 1998 / Accepted: 1 July 1998 / Published: 8 February 1999Abstract: The inorganic property (I) and organic property (O) values of general organicgroups are re-proposed here. Both I and O values of drug and biological molecules orgroups can be calculated based on their common group values. The calculation can beperformed easily on-line through JavaScript. Similar calculation can be done for the drugand biological molecular group electronegativity (X) according to the author's publishedpaper. The calculation of lipophilicity (π or logP) parameter of (macro)molecules (likeproteins) can also be performed on-line through JavaScript. Two equations expressed withI and O are provided here to define the hydrophobicity of each amino acid. Thecorrelations of inorganic property and organic property values with other parameters arealso discussed. These calculated parameters combined with other parameters can be usedfor QSAR studies in some drug molecules.Keywords: Inorganic property and organic property, logP, bio-active molecules,JavaScript, QSAR, drug design.IntroductionQuantitative structure-activity relationship (QSAR) correlation has been widely applied inbiological activities over several decades. Many new descriptors (parameters) have been developed
Novel Piezoelectric DDVP Sensor Based on Self‐Assembly Method

Abstract A novel piezoelectric sensor was fabricated by depositing the films of polyvinyl pyrrolindone (PVP) and PVP with butyl cholinesterase (BuChE) (BuChE‐PVP), respectively, on the surface of quartz crystal microbalance (QCM) using self‐assembly method. The sensitive films were characterized with online examination and XPS technique. The obtained sensors were applied to measure o,o‐dimethyl‐o‐2,2‐dichlorovinyl phosphate (DDVP) pesticide concentration. It shows that BuChE‐PVP sensor has higher sensitivity than PVP sensor. There is a linear relationship between the shift of frequency and the concentration of DDVP in a range from 4.25 to 21.25 ppm. This highly sensitive sensor has also been shown to determine DDVP concentration in several samples, and the relative standard error is less than 3%. It also shows that CO, NO2, H2S, and SO2 with 10 times the concentration of DDVP have no interference with DDVP detection in the tested concentration range.
Study on preparation of chlorinated natural rubber from latex and its thermal stability

The effects of pH value of reaction system, reaction time, and reaction temperature on the chlorination reaction in the preparation of chlorinated natural rubber (CNR) from natural rubber latex were discussed. It has been found from the thermal analysis that the thermal degradation of CNR in nitrogen is a one-step reaction, and 30% carbonide with a stable structure remained at 360 to 700°C; whereas the thermo-oxidative degradation of CNR in air is a multistep reaction, and the thermal degradation ratio reaches to 100% at 560°C. © 1999 John Wiley & Sons, Inc. J Appl Polym Sci 73: 2863–2867, 1999
On the threshold Voltage of symmetrical DG MOS capacitor with intrinsic silicon body

An analytical expression explicitly relating the potential and the electric field at the oxide-semiconductor interface of a symmetrical double-gate oxide-intrinsic semiconductor-oxide system is derived. The expression is continuously applicable to all regimes of operation. The "turn-on" behavior of the system is studied and an "extrapolated" threshold voltage is uniquely defined. Opposite to the behavior of a conventional bulk metal-oxide-semiconductor capacitor realized on a doped substrate, this threshold voltage is shown to decrease with increasing oxide thickness.
Shape evolution of flag types of silver nanostructures from nanorod seeds in PVP-assisted DMF solution

Shape evolution of novel flag types of silver nanostructures has been studied using a two-step reduction method. In the first step, pentagonal Ag nanorods were prepared by polyol reduction of AgNO3 in ethylene glycol (EG) in the presence of polyvinylpyrrolidone (PVP). In the second step, AgNO3 was reduced in N,N-dimethylformamide (DMF) in the presence of Ag nanorod-seeds and PVP. Then, trapezoid and triangular platelike structures having {111} facets were evolved from long side {100} facets of Ag nanorods. At higher AgNO3 concentrations, tetrahedral flags having {111} facets were evolved from equilateral triangular plates. The crystal structures of flag types of Ag nanostructures are determined, and their growth mechanisms are discussed on the basis of TEM, SEM, SAED, and TEM-EDS data. It was found that tetrahedral flags were grown either through the formation of two equilateral triangular plates connected on a nanorod or through the crystal growth of triangular pyramidal structures over triangular plates.
Revisiting pulsar velocities using Gaia Data Release 2

Precise measurements of neutron star (NS) velocities provide critical clues in regard to the supernova physics and evolution of binary systems. Based on Gaia Data Release 2 (DR2), we selected a sample of 24 young (< 3 Myr) pulsars with precise parallax measurements and measured the velocity of their local standard of rest (LSR) and the velocity dispersion among their respective local stellar groups. The median velocity difference between thus calculated LSRs and the Galactic rotation model is ∼ 7.6 km s−1, small compared to the typical velocity dispersion of ∼ 27.5 km s−1. For pulsars off the Galactic plane, such differences grow significantly to as large as ∼40 km s−1. More importantly, the velocity dispersion of stars in the local group of low-velocity pulsars can be comparable to their transverse velocities, suggesting that the intrinsic velocities of NS progenitors should be taken into account when we consider their natal kicks and binary evolution. We also examined the double NS system J0737–3039A/B, and measured its transverse velocity to be 26−13+18 km s−1 assuming nearby Gaia sources are representative of their birth environment. This work demonstrated the feasibility and importance of using Gaia data to study the velocity of individual systems and velocity distribution of NSs.
INTERSPEECH 2021 ConferencingSpeech Challenge: Towards Far-field Multi-Channel Speech Enhancement for Video Conferencing

The ConferencingSpeech 2021 challenge is proposed to stimulate research on far-field multi-channel speech enhancement for video conferencing. The challenge consists of two separate tasks: 1) Task 1 is multi-channel speech enhancement with single microphone array and focusing on practical application with real-time requirement and 2) Task 2 is multi-channel speech enhancement with multiple distributed microphone arrays, which is a non-real-time track and does not have any constraints so that participants could explore any algorithms to obtain high speech quality. Targeting the real video conferencing room application, the challenge database was recorded from real speakers and all recording facilities were located by following the real setup of conferencing room. In this challenge, we open-sourced the list of open source clean speech and noise datasets, simulation scripts, and a baseline system for participants to develop their own system. The final ranking of the challenge will be decided by the subjective evaluation which is performed using Absolute Category Ratings (ACR) to estimate Mean Opinion Score (MOS), speech MOS (S-MOS), and noise MOS (N-MOS). This paper describes the challenge, tasks, datasets, and subjective evaluation. The baseline system which is a complex ratio mask based neural network and its experimental results are also presented.
Fast and Accurate Randomized Algorithms for Low-rank Tensor Decompositions

Low-rank Tucker and CP tensor decompositions are powerful tools in data analytics. The widely used alternating least squares (ALS) method, which solves a sequence of over-determined least squares subproblems, is costly for large and sparse tensors. We propose a fast and accurate sketched ALS algorithm for Tucker decomposition, which solves a sequence of sketched rank-constrained linear least squares subproblems. Theoretical sketch size upper bounds are provided to achieve $O(\epsilon)$ relative error for each subproblem with two sketching techniques, TensorSketch and leverage score sampling. Experimental results show that this new ALS algorithm, combined with a new initialization scheme based on randomized range finder, yields up to $22.0\%$ relative decomposition residual improvement compared to the state-of-the-art sketched randomized algorithm for Tucker decomposition of various synthetic and real datasets. This Tucker-ALS algorithm is further used to accelerate CP decomposition, by using randomized Tucker compression followed by CP decomposition of the Tucker core tensor. Experimental results show that this algorithm not only converges faster, but also yields more accurate CP decompositions.
Limiting Tags Fosters Efficiency

Tagging facilitates information retrieval in social media and other online communities by allowing users to organize and describe online content. Researchers found that the efficiency of tagging systems steadily decreases over time, because tags become less precise in identifying specific documents, i.e., they lose their descriptiveness. However, previous works did not answer how or even whether community managers can improve the efficiency of tags. In this work, we use information-theoretic measures to track the descriptive and retrieval efficiency of tags on Stack Overflow, a question-answering system that strictly limits the number of tags users can specify per question. We observe that tagging efficiency stabilizes over time, while tag content and descriptiveness both increase. To explain this observation, we hypothesize that limiting the number of tags fosters novelty and diversity in tag usage, two properties which are both beneficial for tagging efficiency. To provide qualitative evidence supporting our hypothesis, we present a statistical model of tagging that demonstrates how novelty and diversity lead to greater tag efficiency in the long run. Our work offers insights into policies to improve information organization and retrieval in online communities.
SMET: Scenario-based Metamorphic Testing for Autonomous Driving Models

To improve the security and robustness of autonomous driving models, this paper presents SMET, a scenariobased metamorphic testing tool for autonomous driving models. The metamorphic relationship is divided into three dimensions (time, space, and event) and demonstrates its effectiveness through case studies in two types of autonomous driving models with different outputs.Experimental results show that this tool canwelldetectpotentialdefectsoftheautonomousdrivingmodel, and complex scenes are more effective than simple scenes.
GAVIN

Annotation is an effective reading strategy people often undertake while interacting with digital text. It involves highlighting pieces of text and making notes about them. Annotating while reading in a desktop environment is considered trivial but, in a mobile setting where people read while hand-holding devices, the task of highlighting and typing notes on a mobile display is challenging. In this article, we introduce GAVIN, a gaze-assisted voice note-taking application, which enables readers to seamlessly take voice notes on digital documents by implicitly anchoring them to text passages. We first conducted a contextual enquiry focusing on participants’ note-taking practices on digital documents. Using these findings, we propose a method which leverages eye-tracking and machine learning techniques to annotate voice notes with reference text passages. To evaluate our approach, we recruited 32 participants performing voice note-taking. Following, we trained a classifier on the data collected to predict text passage where participants made voice notes. Lastly, we employed the classifier to built GAVIN and conducted a user study to demonstrate the feasibility of the system. This research demonstrates the feasibility of using gaze as a resource for implicit anchoring of voice notes, enabling the design of systems that allow users to record voice notes with minimal effort and high accuracy.
Streaming Social Event Detection and Evolution Discovery in Heterogeneous Information Networks

Events are happening in real world and real time, which can be planned and organized for occasions, such as social gatherings, festival celebrations, influential meetings, or sports activities. Social media platforms generate a lot of real-time text information regarding public events with different topics. However, mining social events is challenging because events typically exhibit heterogeneous texture and metadata are often ambiguous. In this article, we first design a novel event-based meta-schema to characterize the semantic relatedness of social events and then build an event-based heterogeneous information network (HIN) integrating information from external knowledge base. Second, we propose a novel Pairwise Popularity Graph Convolutional Network, named as PP-GCN, based on weighted meta-path instance similarity and textual semantic representation as inputs, to perform fine-grained social event categorization and learn the optimal weights of meta-paths in different tasks. Third, we propose a streaming social event detection and evolution discovery framework for HINs based on meta-path similarity search, historical information about meta-paths, and heterogeneous DBSCAN clustering method. Comprehensive experiments on real-world streaming social text data are conducted to compare various social event detection and evolution discovery algorithms. Experimental results demonstrate that our proposed framework outperforms other alternative social event detection and evolution discovery techniques.
Do RNN States Encode Abstract Phonological Processes?

Sequence-to-sequence models have delivered impressive results in word formation tasks such as morphological inflection, often learning to model subtle morphophonological details with limited training data. Despite the performance, the opacity of neural models makes it difficult to determine whether complex generalizations are learned, or whether a kind of separate rote memorization of each morphophonological process takes place. To investigate whether complex alternations are simply memorized or whether there is some level of generalization across related sound changes in a sequence-to-sequence model, we perform several experiments on Finnish consonant gradation -- a complex set of sound changes triggered in some words by certain suffixes. We find that our models often -- though not always -- encode 17 different consonant gradation processes in a handful of dimensions in the RNN. We also show that by scaling the activations in these dimensions we can control whether consonant gradation occurs and the direction of the gradation.
How Powerful are Performance Predictors in Neural Architecture Search?

Early methods in the rapidly developing field of neural architecture search (NAS) required fully training thousands of neural networks. To reduce this extreme computational cost, dozens of techniques have since been proposed to predict the final performance of neural architectures. Despite the success of such performance prediction methods, it is not well-understood how different families of techniques compare to one another, due to the lack of an agreed-upon evaluation metric and optimization for different constraints on the initialization time and query time. In this work, we give the first large-scale study of performance predictors by analyzing 31 techniques ranging from learning curve extrapolation, to weight-sharing, to supervised learning, to"zero-cost"proxies. We test a number of correlation- and rank-based performance measures in a variety of settings, as well as the ability of each technique to speed up predictor-based NAS frameworks. Our results act as recommendations for the best predictors to use in different settings, and we show that certain families of predictors can be combined to achieve even better predictive power, opening up promising research directions. Our code, featuring a library of 31 performance predictors, is available at https://github.com/automl/naslib.
Deep ensembles based on Stochastic Activation Selection for Polyp Segmentation

Semantic segmentation has a wide array of applications ranging from medical-image analysis, scene understanding, autonomous driving and robotic navigation. This work deals with medical image segmentation and in particular with accurate polyp detection and segmentation during colonoscopy examinations. Several convolutional neural network architectures have been proposed to effectively deal with this task and with the problem of segmenting objects at different scale input. The basic architecture in image segmentation consists of an encoder and a decoder: the first uses convolutional filters to extract features from the image, the second is responsible for generating the final output. In this work, we compare some variant of the DeepLab architecture obtained by varying the decoder backbone. We compare several decoder architectures, including ResNet, Xception, EfficentNet, MobileNet and we perturb their layers by substituting ReLU activation layers with other functions. The resulting methods are used to create deep ensembles which are shown to be very effective. Our experimental evaluations show that our best ensemble produces good segmentation results by achieving high evaluation scores with a dice coefficient of 0.884, and a mean Intersection over Union (mIoU) of 0.818 for the Kvasir-SEG dataset. To improve reproducibility and research efficiency the MATLAB source code used for this research is available at GitHub: https://github.com/LorisNanni.
Surface Studies of Passivated Molybdenum Oxynitride

Molybdenum oxynitrides are very sensitive and attention should be paid to their storage and pretreatment in order to get accurate and reproducible physisorption or chemisorption results. This work showed that storage of these compounds in air leads to loss of accessibility of the surface area, illustrated by an apparent decrease of the specific surface area and pore volume. This phenomenon is due to the high affinity of these materials for oxygen, which diffuses into their lattice. Furthermore, oxygen, ammonia, and water, present on the surface of molybdenum oxynitrides after preparation and passivation, can block the access of probe molecules to pores or active sites. A method of storage and pretreatment before physisorption or chemisorption experiments is presented. CO chemisorption experiments in static and dynamic pulsed conditions were performed and led to similar results as in experiments performed with samples without any residual oxygen contamination. Low CO uptakes were interpreted in terms of nonstoichiometry of the compounds.
What Taggers Fail to Learn, Parsers Need the Most

We present an error analysis of neural UPOS taggers to evaluate why using gold tags has such a large positive contribution to parsing performance while using predicted UPOS either harms performance or offers a negligible improvement. We also evaluate what neural dependency parsers implicitly learn about word types and how this relates to the errors taggers make, to explain the minimal impact using predicted tags has on parsers. We then mask UPOS tags based on errors made by taggers to tease away the contribution of UPOS tags that taggers succeed and fail to classify correctly and the impact of tagging errors.
Improved Accuracy and Precision In Simultaneous Myocardial T1 and T2 mapping with Multi-Parametric SASHA (mSASHA)

Improved Accuracy and Precision In Simultaneous Myocardial T1 and T2 mapping with MultiParametric SASHA (mSASHA) Kelvin Chow, Genevieve Hayes, Jacqueline A Flewitt, Patricia Feuchter, Carmen Lydell, Andrew Howarth, Joseph J Pagano, Richard B Thompson, Peter Kellman, James A White Cardiovascular MR R&D, Siemens Medical Solutions USA, Inc., Chicago IL, USA Stephenson Cardiac Imaging Centre, University of Calgary, Calgary AB, Canada Division of Pediatric Cardiology, University of Alberta, Edmonton AB, Canada Department of Biomedical Engineering, University of Alberta, Edmonton AB, Canada National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda MD, USA
Three-Dimensional Trapping and Dynamic Axial Manipulation with Frequency-Tuned Spiraling Acoustical Tweezers: A Theoretical Study

Holographic acoustical tweezers (HAT) based on Archimedes-Fermat spiraling InterDigitated Transducers (S-IDTs) are a versatile tool for the selective manipulation of microparticles [Baudoin et. al., Sci. Adv. , 5: eaav1967 (2019)] and cells [Baudoin et. al., Nat. Commu. , 11, 4244 (2020)] in a standard microfluidic environment. These binary active holograms produce some focused helical wave, with the ability to trap particles at the vortex core. Yet, all the studies conducted with S-IDTs have so far been restricted to 2D manipulation only. Here we show (i) that 3D radiation trap for microparticles and cells can be obtained with spiraling tweezers with sufficiently large aperture and (ii) that the particles can be displaced axially by simply tuning the driving frequency, without any motion of the transducer. This work opens perspectives for 3D cells and microparticles manipulation with single-beam acoustical tweezers.
Magnetic and tidal migration of close-in planets. Influence of secular evolution on their population

Over the last two decades, a large population of close-in planets has been detected around a wide variety of host stars. Such exoplanets are likely to undergo planetary migration through magnetic and tidal interactions. We aim to follow the orbital evolution of a planet along the structural and rotational evolution of its host star by taking into account simultaneously tidal and magnetic torques, in order to explain some properties of the distribution of observed close-in planets. We rely on a numerical model of a coplanar circular star-planet system taking into account stellar structural changes, wind braking and star-planet interactions, called ESPEM. We browse the parameter space of star-planet systems' configurations and assess the relative influence of magnetic and tidal torques on secular evolution. We then synthesize star-planet populations and confront their distribution in orbital and stellar rotation periods to Kepler satellite data. First, we find that after the dissipation of the protoplanetary disk, both types of interactions can dominate secular evolution depending on the initial configuration of the system and the evolutionary phase considered. Moreover, different populations of star-planet systems emerge from the combined action of both kinds of interactions, according to the evolutionary phase during which the planet migrates significantly. This may affect significantly the detectability of star-planet systems as well as the validity of gyrochonology. All in all, star-planet interactions significantly impact the global distribution in orbital periods during the main sequence, while the global distribution in stellar rotation periods is marginally affected. More precisely, star-planet magnetic interactions significantly affect the distribution of super-Earths around slowly rotating stars, while tidal effects are found to shape the distribution of giant planets.