Miracle-dz/UniFAR-FTUs huggingface dataset:
Variations on the chemical components of the volatile oil of Houttuynia cordata Thunb. populations from different valleys and altitudes of Mt. Emei

The aerial part of H.cordata and the soil of 17 populations were collected from different valleys and altitudes(500-1800m) of Mt.Emei. pH value and soil nutrient contents were determined by regular methods of agricultural chemistry analysis of the soil. Volatile oil was obtained by hydrodistillation from the aerial part of all materials. The chemical constituents were separated and identified by GC-MS, and the relative content of each constituent was determined by area normalization. The results showed that fertility of all the soil samples were higher, which was an advantage to the growth of H. cordata populaions. Totally, thirty-six components were identified. The components and content of volatile oil in different H.cordata populations were various. Myrcene, being the main component, could be detected in all populations, and its average content was higher than that of other chemical components. The content of bornyl acetate had positive correlation with the altitude and the content of trans-β-ocimene had significant positive correlation with the altitude. The results of variance analysis and multiple comparisons showed that the altitude had significant influence on the compositions and contents of volatile oil of H.cordata populations. It was found that microelements have more important influence on volatile oil components than macroelements through partial correlation analysis. Available B is the most important influence factor in all microelements. Available K is the most important influence factor in all macroelements. Soil nutrients which have significant influence on 10 mutual volatile oil components were deduced by stepwise regression analysis.
Extraction and identification of cuticular semiochemical components of Cyrtotrachelus buqueti Guerin-Meneville(Coleoptera:Curculionidae)

The aim of this study is to identify the semiochemicals of the female weevil,Cyrtotrachelus buqueti Guerin-Meneville,an important bamboo forest weevil pest in Southern China.The behavioral responses of male weevils to the crude cuticular extracts from virgin female weevils in a flight tunnel was analyzed,and the results showed that male weevils had positive response to the cuticular extracts as well as to virgin female weevils.The sensitive reaction of male weevil's antenna to the crude cuticular extracts from virgin female weevils was detected by GC-EAD,and the components of the cuticular extracts that consistently elicited antennal responses were identified by GC-MS.The results indicated that male weevil's antenna had sensitive reaction to synthetic standard chemicals of five components in the female extracts,i.e.,phenol,ethyl hexanoate,2-nonanone,nonanal,and pentadecanoic acid methyl ester.Flight tunnel and GC-EAD studies showed that the five synthetic chemicals were also bioactive to male weevils,suggesting that phenol,ethyl hexanoate,2-nonanone,nonanal,and pentadecanoic acid methyl ester are semiochemicals of C.buqueti.
O3-10-03 DEVELOPMENTAND VALIDATION OF THE MEMORY COMPLAINTS QUESTIONNAIRE

MEMORY COMPLAINTS QUESTIONNAIRE Graham J. McDougall, Jr, Safiya D. George, Ian M. McDonough, Lorena Lyon, Mike LaRocca, Kris Arheart, University of Alabama-College of Nursing, Tuscaloosa, AL, USA; University of Alabama, Tuscaloosa, AL, USA; University of Alabama-Department of Psychology, Tuscaloosa, AL, USA; 4 University of Miami-Miller School of Medicine, Miami, FL, USA. Contact e-mail: gjmcdougall@ua.edu
Velogenic Viscerotropic Newcastle Disease Virus Produces Variable Pathogenicity in Two Chicken Breeds

Received: Revised: Accepted: 2014–08–21 2014–09–18 2014–09–19 Newcastle disease has a high prevalence in Pakistan and outbreaks are reported even in vaccinated chickens. Disease resistance of various breeds to Newcastle disease needs to be studied for better husbandry and breeding decisions. The aim of the present study was to compare resistance of two chicken breeds to Velogenic Viscerotropic Newcastle Disease Virus (VVNDV) infection. It was carried by using different pathogenicity tests like mean death time (MDT) of chicken embryos, intracerebral pathogenicity index (ICPI) in day old chicks and intravenous pathogenicity (IVPI) in 6–week old chickens. MDT in VVNDV inoculated embryonated eggs of Fayoumi and White Leghorn breeds was 41.5±16 and 42.1 ±10.3 hours, respectively but difference was not statistically significant. In embryonated eggs of Fayoumi 0.6 log10 higher EID50 and significantly higher (0.8 log2) hemagglutination titers of VVND virus than White Leghorn breed was observed. The ICPI in Fayoumi and White Leghorn, was found to be 0.93 and 0.71 while IVPI in Fayoumi and White Leghorn was found to be 2.51 and 2.65, respectively. Average lesion scores for intravenously infected chickens were 4.8 and 4.9 while in contact exposed chickens scores were 6.6 and 8.4 for Fayoumi and White Leghorn breeds, respectively. Embryos and day–old chicks of Fayoumi were found more susceptible to VVNDV than White Leghorn as they showed lower MDT along with higher HA / EID50 tiers and ICPI, scores than White Leghorn. Both intravenously inoculated as well as contact–exposed chickens of Fayoumi breed were found more resistant as they had lower IVPI and postmortem lesion scores. All copyrights reserved to Nexus® academic publishers
PolyAdd: Polynomial Formal Verification of Adder Circuits

Only by formal verification approaches functional correctness can be ensured. While for many circuits fast verification is possible, in other cases the approaches fail. In general no efficient algorithms can be given, since the underlying verification problem is NP-complete. In this paper we prove that for different types of adder circuits polynomial verification can be ensured based on BDDs. While it is known that the output functions for addition are polynomially bounded, we show in the following that the entire construction process can be carried out in polynomial time. This is shown for the simple Ripple Carry Adder, but also for fast adders like the Conditional Sum Adder and the Carry Look Ahead Adder. Properties about the adder function are proven and the core principle of polynomial verification is described that can also be extended to other classes of functions and circuit realizations.
A Hierarchical Architecture for Human–Robot Cooperation Processes

In this article, we propose FlexHRC+, a hierarchical human–robot cooperation architecture designed to provide collaborative robots with an extended degree of autonomy when supporting human operators in high-variability shop-floor tasks. The architecture encompasses three levels, namely for perception, representation, and action. Building up on previous work, here we focus on an in-the-loop decision-making process for the operations of collaborative robots coping with the variability of actions carried out by human operators, and the representation level, integrating a hierarchical and/or graph whose online behavior is formally specified using first-order logic. The architecture is accompanied by experiments including collaborative furniture assembly and object positioning tasks.
An Online Learning Approach to Dynamic Pricing and Capacity Sizing in Service Systems

Online Learning in Queueing Systems Most queueing models have no analytic solutions, so previous research often resorts to heavy-traffic analysis for performance analysis and optimization, which requires the system scale (e.g., arrival and service rate) to grow to infinity. In “An Online Learning Approach to Dynamic Pricing and Capacity Sizing in Service Systems,” X. Chen, Y. Liu, and G. Hong develop a new “scale-free” online learning framework designed for optimizing a queueing system, called gradient-based online learning in queue (GOLiQ). GOLiQ prescribes an efficient procedure to obtain improved decisions in successive cycles using newly collected queueing data (e.g., arrival counts, waiting times, and busy times). Besides its robustness in the system scale, GOLiQ is advantageous when focusing on performance optimization in the long run because its data-driven nature enables it to constantly produce improved solutions which will eventually reach optimality. Effectiveness of GOLiQ is substantiated by theoretical regret analysis (with a logarithmic regret bound) and simulation experiments.
DeePSD: Automatic Deep Skinning And Pose Space Deformation For 3D Garment Animation

We present a novel solution to the garment animation problem through deep learning. Our contribution allows animating any template outfit with arbitrary topology and geometric complexity. Recent works develop models for garment edition, resizing and animation at the same time by leveraging the support body model (encoding garments as body homotopies). This leads to complex engineering solutions that suffer from scalability, applicability and compatibility. By limiting our scope to garment animation only, we are able to propose a simple model that can animate any outfit, independently of its topology, vertex order or connectivity. Our proposed architecture maps outfits to animated 3D models into the standard format for 3D animation (blend weights and blend shapes matrices), automatically providing of compatibility with any graphics engine. We also propose a methodology to complement supervised learning with an unsupervised physically based learning that implicitly solves collisions and enhances cloth quality.
"Reduction of Monetary Cost in Cloud Storage System by Using Extended Strict Timed Causal Consistency"

Cloud storage systems have been introduced to provide a scalable, secure, reliable, and highly available data storage environment for the organizations and end-users. Therefore, the service provider should grow in a geographical extent. Consequently, extensive storage service provision requires a replication mechanism. Replication imposes many costs on the cloud storage, including the synchronization, communications, storage, etc., costs among the replicas. Moreover, the synchronization process among replicas is a major challenge in cloud storage. Therefore, consistency can be defined as the coordination among the replicas. In this paper, we propose an extension to the strict timed causal consistency by adding the considerations for the monetary costs and the number of violations in the cloud storage systems and call it the extended strict timed causal consistency. Our proposed supports monotonic read, read your write, monotonic write, and write follow read, models by taking into account the causal relations between users' operations, at the client-side. Besides, it supports timed causal at the server-side. We employed the Cassandra cloud database that supports various consistencies such as all, one, quorum, etc. Our method performs better in reducing staleness rate, the severity of violations, and monetary cost in comparison with all, one, quorum, and causal.
Convergence Analysis of the Hessian Estimation Evolution Strategy

Abstract The class of algorithms called Hessian Estimation Evolution Strategies (HE-ESs) update the covariance matrix of their sampling distribution by directly estimating the curvature of the objective function. The approach is practically efficient, as attested by respectable performance on the BBOB testbed, even on rather irregular functions. In this article, we formally prove two strong guarantees for the (1 + 4)-HE-ES, a minimal elitist member of the family: stability of the covariance matrix update, and as a consequence, linear convergence on all convex quadratic problems at a rate that is independent of the problem instance.
Statistics of the spectral form factor in the self-dual kicked Ising model

We compute the full probability distribution of the spectral form factor in the self-dual kicked Ising model by providing an exact lower bound for each moment and verifying numerically that the latter is saturated. We show that at large enough times the probability distribution agrees exactly with the prediction of Random Matrix Theory if one identifies the appropriate ensemble of random matrices. We find that this ensemble is not the circular orthogonal one - composed of symmetric random unitary matrices and associated with time-reversal-invariant evolution operators - but is an ensemble of random matrices on a more restricted symmetric space (depending on the parity of the number of sites this space is either ${Sp(N)/U(N)}$ or ${O(2N)/{O(N)\! \times\! O(N)}}$). Even if the latter ensembles yield the same averaged spectral form factor as the circular orthogonal ensemble they show substantially enhanced fluctuations. This behaviour is due to a recently identified additional anti-unitary symmetry of the self-dual kicked Ising model.
Approaches, Challenges, and Applications for Deep Visual Odometry: Toward Complicated and Emerging Areas

Visual odometry (VO) is a prevalent way to deal with the relative localization problem, which is becoming increasingly mature and accurate, but it tends to be fragile under challenging environments. Comparing with classical geometry-based methods, deep-learning-based methods can automatically learn effective and robust representations, such as depth, optical flow, feature, ego-motion, etc., from data without explicit computation. Nevertheless, there still lacks a thorough review of the recent advances of deep-learning-based VO (Deep VO). Therefore, this article aims to gain a deep insight on how deep learning can profit and optimize the VO systems. We first screen out a number of qualifications, including accuracy, efficiency, scalability, dynamicity, practicability, and extensibility, and employ them as the criteria. Then, using the offered criteria as the uniform measurements, we detailedly evaluate and discuss how deep learning improves the performance of VO from the aspects of depth estimation, feature extraction and matching, and pose estimation. We also summarize the complicated and emerging areas of Deep VO, such as mobile robots, medical robots, augmented and virtual reality, etc. Through the literature decomposition, analysis, and comparison, we finally put forward a number of open issues and raise some future research directions in this field.
Team Alex at CLEF CheckThat! 2020: Identifying Check-Worthy Tweets With Transformer Models

While misinformation and disinformation have been thriving in social media for years, with the emergence of the COVID-19 pandemic, the political and the health misinformation merged, thus elevating the problem to a whole new level and giving rise to the first global infodemic. The fight against this infodemic has many aspects, with fact-checking and debunking false and misleading claims being among the most important ones. Unfortunately, manual fact-checking is time-consuming and automatic fact-checking is resource-intense, which means that we need to pre-filter the input social media posts and to throw out those that do not appear to be check-worthy. With this in mind, here we propose a model for detecting check-worthy tweets about COVID-19, which combines deep contextualized text representations with modeling the social context of the tweet. We further describe a number of additional experiments and comparisons, which we believe should be useful for future research as they provide some indication about what techniques are effective for the task. Our official submission to the English version of CLEF-2020 CheckThat! Task 1, system Team_Alex, was ranked second with a MAP score of 0.8034, which is almost tied with the wining system, lagging behind by just 0.003 MAP points absolute.
Complementary dynamics

We present a novel approach to enrich arbitrary rig animations with elastodynamic secondary effects. Unlike previous methods which pit rig displacements and physical forces as adversaries against each other, we advocate that physics should complement artists' intentions. We propose optimizing for elastodynamic displacements in the subspace orthogonal to displacements that can be created by the rig. This ensures that the additional dynamic motions do not undo the rig animation. The complementary space is high-dimensional, algebraically constructed without manual oversight, and capable of rich high-frequency dynamics. Unlike prior tracking methods, we do not require extra painted weights, segmentation into fixed and free regions or tracking clusters. Our method is agnostic to the physical model and plugs into non-linear FEM simulations, geometric as-rigid-as-possible energies, or mass-spring models. Our method does not require a particular type of rig and adds secondary effects to skeletal animations, cage-based deformations, wire deformers, motion capture data, and rigid-body simulations.
Spectroscopic Detection of the Pre-White Dwarf Companion of Regulus

Mass transfer in an interacting binary will often strip the mass donor of its entire envelope and spin up the mass gainer to near critical rotation. The nearby B-type star Regulus represents a binary in the post-mass transfer stage: it is a rapid rotator with a very faint companion in a 40 days orbit. Here we present the results of a search for the spectral features of the stripped-down star in an extensive set of spectra with high signal-to-noise ratio and high resolution obtained with the CFHT/ESPaDOnS and TBL/NARVAL spectrographs. We first determine revised orbital elements in order to set accurate estimates of the orbital Doppler shifts at the times of observation. We then calculate cross-correlation functions of the observed and model spectra, and we search for evidence of the companion signal in the residuals after removal of the strong primary component. We detect a weak peak in the co-added residuals that has the properties expected for a faint pre-white dwarf. We use the dependence of the peak height and width on assumed secondary velocity semiamplitude to derive the semiamplitude, which yields masses of M1/M⊙ = 3.7 ± 1.4 and M2/M⊙ = 0.31 ± 0.10 (assuming orbital inclination equals the spin inclination of Regulus). We estimate the temperature of the pre-white dwarf Teff = (20 ± 4) kK through tests with differing temperature model spectra, and we find the radius R2/R⊙ = 0.061 ± 0.011 from the component temperatures and the flux ratio associated with the amplitude of the signal in the cross-correlation residuals.
Personalized Review Ranking for Improving Shopper's Decision Making: A Term Frequency based Approach

User-generated reviews serve as crucial references in shopper's decision-making process. Moreover, they improve product sales and validate the reputation of the website as a whole. Thus, it becomes important to design reviews ranking methods that help shoppers make informed decisions quickly. However, reviews ranking has its unique challenges. First, there is no relevance labels for reviews. A relevant review for shopper A might not be relevant to shopper B. Second, since shoppers cannot click on reviews, we have no ways of getting relevance feedback. Eventually, reviews ranking suffers from the lack of ground truth due to the variability in the standard of relevance for different users. In this paper, we aim to address the challenges of helping users to find information they might be interested in from the sea of customer reviews. Using the Amazon Customer Reviews Dataset collected and organized by UCSD, we first constructed user profiles based on user's personal web trails, recent shopping history and previous reviews, incorporated user profiles into our ranking algorithm, and assigned higher ranks to reviews that address individual shopper's concerns to the largest extent. Also, we leveraged user profiles to recommend products based on reviews texts. We evaluated our model based on both empirical evaluations and numerical evaluations of review scores. The results from both evaluation methods reveal a significant increase in the quality of top reviews as well as user satisfaction for over 1000 products. Our reviews based recommendation system also suggests that there's a large chance of user viewing and liking the product we recommend. Our work shows the basic steps of developing a ranking method that learns from a particular end-user's preferences.
Personal Augmented Reality for Information Visualization on Large Interactive Displays

In this work we propose the combination of large interactive displays with personal head-mounted Augmented Reality (AR) for information visualization to facilitate data exploration and analysis. Even though large displays provide more display space, they are challenging with regard to perception, effective multi-user support, and managing data density and complexity. To address these issues and illustrate our proposed setup, we contribute an extensive design space comprising first, the spatial alignment of display, visualizations, and objects in AR space. Next, we discuss which parts of a visualization can be augmented. Finally, we analyze how AR can be used to display personal views in order to show additional information and to minimize the mutual disturbance of data analysts. Based on this conceptual foundation, we present a number of exemplary techniques for extending visualizations with AR and discuss their relation to our design space. We further describe how these techniques address typical visualization problems that we have identified during our literature research. To examine our concepts, we introduce a generic AR visualization framework as well as a prototype implementing several example techniques. In order to demonstrate their potential, we further present a use case walkthrough in which we analyze a movie data set. From these experiences, we conclude that the contributed techniques can be useful in exploring and understanding multivariate data. We are convinced that the extension of large displays with AR for information visualization has a great potential for data analysis and sense-making.
Main-sequence Scatter is Real: The Joint Dependence of Galaxy Clustering on Star Formation and Stellar Mass

We present new measurements of the clustering of stellar-mass-complete samples of ∼40,000 SDSS galaxies at z ∼ 0.03 as a joint function of stellar mass and specific star formation rate (sSFR). Our results confirm what Coil et al. find at z ∼ 0.7: galaxy clustering is a stronger function of sSFR at fixed stellar mass than of stellar mass at fixed sSFR. We also find that galaxies above the star-forming main sequence (SFMS) with higher sSFR are less clustered than galaxies below the SFMS with lower sSFR, at a given stellar mass. A similar trend is present for quiescent galaxies. This confirms that main-sequence scatter, and scatter within the quiescent sequence, is physically connected to the large-scale cosmic density field. We compare the resulting galaxy bias versus sSFR, and relative bias versus sSFR ratio, for different galaxy samples across 0 < z < 1.2 to mock galaxy catalogs based on the empirical galaxy evolution model of Behroozi et al. This model fits PRIMUS and DEEP2 clustering data well at intermediate redshift, but agreement with SDSS is not as strong. We show that increasing the correlation between galaxy SFR and halo accretion rate at z ∼ 0 in the model substantially improves agreement with SDSS data. Mock catalogs suggest that central galaxies contribute substantially to the dependence of clustering on sSFR at a given stellar mass and that the signal is not simply an effect of satellite galaxy fraction differences with sSFR. Our results are highly constraining for galaxy evolution models and show that the stellar-to-halo mass relation depends on sSFR.