Miracle-dz/UniFAR-FTUs huggingface dataset:
Biomass estimation of desert steppe based on spectral indices along a precipitation gradient

Abstract Accurate estimation of grassland biomass in desert steppe is crucial for various studies such as grassland dynamic monitoring, scientific management, and environmental degradation. Observational tests were conducted in the Ordos desert steppe in July and August from 2014 to 2016 to measure the grassland biomass and grassland canopy spectral data along a precipitation gradient, to analyze the spectral reflectance characteristics and the correlation between different spectral indices and grassland biomass in the range of 350–1830 nm, and to establish and verify a model for estimating grassland biomass of desert steppe. The results showed that the spectral reflectance of the Ordos desert steppe is similar to the spectral characteristics of typical green vegetation. The spectral reflectance values of control and of lightly, medium-, and heavily degraded plots successively increased in the visible light band at 350–696 nm and decreased in the near-infrared band at 792–1349 nm. The sensitive band of the normalized difference spectrum indices after optimization was 705 and 710. The coefficient of determination of normalized difference spectral indices (705 and 710) and grassland biomass was R2 = 0.735. These values were significantly correlated at the p < 0.01 level. The R2 values of the fitted power function model and the verification power function model were 0.764 and 0.746, respectively. The root mean square error was 41.59 g/m2. The relative error was 28.37%, and the constructed power function model was the best model. Therefore, the optimized spectral indices can be used to effectively estimate the grassland biomass of desert steppe.
Application of the DNAS framework expansion to occupant population synthesis

Research in occupant behaviour is now using a more elaborate framework of building occupant interaction. Researchers often face challenges in collecting data, particularly for the data to meet the minimum number of required data points and the data interoperability requirements. Researchers address the first issue with the synthetic population and the latter with data ontologies. While synthetic population is commonly used to address the first issue, data ontology development is used to address the latter. The two solutions are complementary to each other. One of the known ontologies in building occupant behaviour research is the Drivers-Needs-Actions-Systems (DNAS) ontology, which has been used by building modelers to describe energy-related occupant behaviour. This paper describes the ontology-based synthetic population generation that can be used in the agent-based modeling (ABM) applications. This paper considers multiple data sources, including ASHRAE Thermal Comfort DB II and IEA Annex 66 data sets. A case study of an office building is used to present the workflow of DNAS framework expansion, synthetic population generation, and agent-based modeling.
Deep Quaternion Pose Proposals for 6D Object Pose Tracking

In this work we study quaternion pose distributions for tracking in RGB image sequences the 6D pose of an object selected from a set of objects, for which common models were trained in advance. We propose an unit quaternion representation of the rotational state space for a particle filter, which is then integrated with the particle swarm optimization to shift samples toward local maximas. Owing to k-means++ we better maintain multimodal probability distributions. We train convolutional neural networks to estimate the 2D positions of fiducial points and then to determine PnP-based object pose hypothesis. A CNN is utilized to estimate the positions of fiducial points in order to calculate PnP-based object pose hypothesis. A common Siamese neural network for all objects, which is trained on keypoints from current and previous frame is employed to guide the particles towards predicted pose of the object. Such a key-point based pose hypothesis is injected into the probability distribution that is recursively updated in a Bayesian framework. The 6D object pose tracker is evaluated on Nvidia Jetson AGX Xavier both on synthetic and real sequences of images acquired from a calibrated RGB camera.
COMPARISON OF THE INTERLEUKIN-33 AMONG THE HEALTHY AND AGGRESSIVE PERIODONTITIS CASES: AN ORIGINAL RESEARCH

Introduction: Interleukin-33 (IL-33) is a novel alarmin that warns immune cells of tissue destruction in injury or infection. Hence in this study we aim to compare the interleukin-3 among the healthy and aggressive periodontitis cases. Materials and Methods: We conducted a cross-sectional, biochemical, genetic study. We divided the groups to three as healthy group, chronic periodontitis patients, and generalized aggressive periodontitis patients. IL33 concentration in GCF, as well as plasma, was quantified. Results: A significant difference was found in IL-33 concentration in GCF and plasma between the three groups. GG genotype of IL-33 SNP rs1157505 was associated with the highest GCF and plasma IL-33 concentration and was significantly more in GAgP than healthy or CP groups. IL-33 SNP rs7044343 did not show any such association. All Gag Ppatients had the highest GCF and plasma concentrationofIL-33. Conclusions: IL-33 may be a potential inflammatory marker of periodontitis. GG genotype of SNP rs1157505 may be associated with generalized aggressive periodontitis.
Introducing Credibility Signals and Citations to Video-Sharing Platforms

Misinformation is a growing problem in today’s online information ecosystem. In particular, on major video-sharing platforms, there are few signals for users to assess the credibility of the videos they encounter, even as more people turn towards them for getting information. In this work, we propose a series of designs to indicate credibility on video-sharing platforms, including via signals on video search result pages, as well as citations on video pages, using the major platform YouTube. From an initial interview study on how users perceive YouTube credibility indicators, we found that video intent and context heavily shape how a user would apply credibility signals or citations. Along with an additional planned survey, these results inform design decisions for a browser extension we will build and deploy to test our designs.
Extensive human-mediated jump dispersal within and across the native and introduced 1 ranges of the invasive termite

26 As native ranges are often geographically structured, invasive species originating from a single 27 source population only carry a fraction of the genetic diversity present in their native range. The 28 invasion process is thus often associated with a drastic loss of genetic diversity resulting from a 29 founder event. However, the fraction of diversity brought to the invasive range may vary under 30 different invasion histories, increasing with the size of the propagule, the number of re-introduction 31 events, and/or the total genetic diversity represented by the various source populations in a 32 multiple-introduction scenario. In this study, we generated a SNP dataset for the invasive termite 33 Reticulitermes flavipes from 23 native populations in the eastern United States and six introduced 34 populations throughout the world. Using population genetic analyses and approximate Bayesian 35 computation Random Forest, we investigated its worldwide invasion history. We found a complex 36 invasion pathway with multiple events out of the native range and bridgehead introductions from 37 the introduced population in France. Our data suggest that extensive long-distance jump dispersal 38 appears common in both the native and introduced ranges of this species, likely through human 39 transportation. Overall, our results show that similar to multiple introduction events into the 40 invasive range, admixture in the native range prior to invasion can potentially favor invasion 41 success by increasing the genetic diversity that is later transferred to the introduced range. 42
Body Composition and Segmental Phase Angle in Physically Active Men

: Body composition (BC) is strongly related to the overall health and ﬁtness levels of athletes. The aim of this study was to evaluate BC and in particular phase angle for whole body and for the different body segments in males elite athletes compared with a normal-weight control group. 27 young men participated in the study: 9 ciclysts (age 28.8±3.5 years; weight 70.2±5.6kg; BMI 21.2±1.2 kg / m 2 ), 9 dancers (age 19.2±1.3 years; weight 63.3±5.8 kg; BMI 20.8±1.0kg /m 2 ) and 9 young normal-weight men (age 18.9±2.8years, weight 70.7±11.6 kg, BMI 22.9±3.3 kg / m 2. ) Bioimpedance analysis (BIA) was performed at 50 kHz (DS Medica) early in the morning. Phase angle (a bioimpedance variable related to nutritional status) was used in order to evaluate differences in fat free mass (FFM) characteristics among the groups. As far as BC, cyclists showed the highest values of FFM whereas dancers and controls were similar. Fat mass (FM), both in absolute value and in percentage, is significantly lower in dancers and cyclists than controls. Total phase angle and leg phase angle were similar in Cyclist, dancers and controls. Regarding arm phase angle, there were no significantly differences between groups. This study shows that the sports activity, regardless of the type of sport, had a significant effect on BC variables respect non-athletic young men.
Transforming Existing Stateful Network Protocol Applications into Effective Protocol-Specific Security Testing Tools

Transforming Existing Stateful Network Protocol Applications into Effective Protocol-Specific Security Testing Tools Vasily Rudchenko Master of Applied Science Graduate Department of Electrical and Computer Engineering University of Toronto 2019 To discover low-level security vulnerabilities in target applications that implement stateful network protocols, security researchers usually employ manually-constructed protocol-specific testing tools. Due to the complexity and heterogeneity of network protocols, however, building such tools for every network protocol used by target programs is a challenging task. We propose a semi-automated technique for creating protocol-specific testing tools, which leverages an existing application that implements a network protocol (a source), and converts it into a testing tool that can be used for bug discovery in any target program that “speaks” the same protocol. We call our implementation of this method InSource, and, by measuring coverage gain, show that InSource can test a target just as well a manually-created protocolspecific testing tool. Furthermore, we show that InSource is effective at testing applications that use different protocols, as long as a source application for each protocol is available.
Biomass Recovery Method for Adenosine Triphosphate (ATP) Quantification Following UV Disinfection

ABSTRACT A biomass recovery method was developed to monitor UV disinfection efficacy using adenosine triphosphate (ATP). Typically, disinfection monitoring at wastewater treatment facilities (WWTFs) involves quantifying fecal and total coliforms or colony forming units, the results of which take a minimum of 24 h to produce. ATP quantification immediately before and after UV treatment, which takes only minutes, shows little reduction and often an increase in the microbial population since UV irradiation results in cells that are viable (i.e., still producing ATP) but not culturable. To overcome this, our biomass recovery method incorporates an incubation step to encourage life cycling of microbes. Average log reductions in cellular ATP (cATP) were found to be −0.28 ± 0.19, −0.011 ± 0.153, −0.17 ± 0.32, and 0.065 ± 0.074 using direct ATP measurements on UV-treated samples from WWTFs A, B, C, and D, respectively, while those using the recovery method were correspondingly 0.17 ± 0.34, 1.8 ± 0.8, 0.20 ± 0.35, and 0.72 ± 0.26. The response of the biomass recovery-ATP method indicated a significant direct correlation to the microbial population reduction observed in heterotrophic plate count (HPC) and Colilert® methods using both pure Escherichia coli culture and secondary municipal wastewater effluent.
IoT-Based Accompanying Robot

—This paper designs an internet of things (IoT)-Based accompanying robot. The robot can help users to accompany their children or pets through internet when they go out. An camera and a screen are settled on the robot to achieve the function of video call. A infrared camera is settle on the back of the robot for automatic homing charging. Moreover, a feeding device is designed for giving cookies to children or feeding pets when playing with them. Through the IoT-Based robot, users can not only accompany their children or pets but also monitor their home when they go out.
Potential Use of DNA Aptamer-Magnetic Bead Separation-PCR Assay for Salmonella Detection in Food

Background: Salmonella is one of the most common food-borne pathogens that can cause illness. In this study, the sensitivity and the specificity of Aptamer-Magnetic bead Separation-Polymerase Chain Reaction (AMS-PCR) method were determined for Salmonella spp. detection. Methods: Different concentrations of Salmonella enterica were mixed with streptavidinmagnetic beads coated with biotinylated DNA aptamer. The bound bacteria were eluted and tested with PCR targeting the invA gene of Salmonella. Ten different serovars of Salmonella enterica and four non-Salmonella were tested to determine the specificity of the DNA aptamer. For field application, 14 different food samples were tested and compared with the culture method. Results: The limit of detection of AMS-PCR method was 10 2 CFU/ml which was 10 times more sensitive than conventional PCR without AMS (10 3 CFU/ml). The AMS-PCR assay showed high specificity as it detected ten different serovars of Salmonella enterica with no cross-reactivity with other food-borne pathogens. AMS-PCR reduced the analytical duration from 6 to 7 h instead of 4 days by the culture method. Conclusion: In comparison with the culture method, AMS helped to improve the upstream sample preparation in reducing the pre-enrichment and enrichment times. So, it seems that combining AMS with PCR is cost-effective and time-saving. In addition, it is highly specific for monitoring of Salmonella spp. in food chain. © 2018, Shahid Sadoughi University of Medical Sciences. This is an open access article under the Creative Commons Attribution 4.0 International License.
Beyond Matérn: On A Class of Interpretable Confluent Hypergeometric Covariance Functions

Abstract The Matérn covariance function is a popular choice for prediction in spatial statistics and uncertainty quantification literature. A key benefit of the Matérn class is that it is possible to get precise control over the degree of mean-square differentiability of the random process. However, the Matérn class possesses exponentially decaying tails, and thus, may not be suitable for modeling polynomially decaying dependence. This problem can be remedied using polynomial covariances; however, one loses control over the degree of mean-square differentiability of corresponding processes, in that random processes with existing polynomial covariances are either infinitely mean-square differentiable or nowhere mean-square differentiable at all. We construct a new family of covariance functions called the Confluent Hypergeometric (CH) class using a scale mixture representation of the Matérn class where one obtains the benefits of both Matérn and polynomial covariances. The resultant covariance contains two parameters: one controls the degree of mean-square differentiability near the origin and the other controls the tail heaviness, independently of each other. Using a spectral representation, we derive theoretical properties of this new covariance including equivalent measures and asymptotic behavior of the maximum likelihood estimators under infill asymptotics. The improved theoretical properties of the CH class are verified via extensive simulations. Application using NASA’s Orbiting Carbon Observatory-2 satellite data confirms the advantage of the CH class over the Matérn class, especially in extrapolative settings. Supplementary materials for this article are available online.
Towards the 5/6-Density Conjecture of Pinwheel Scheduling

Pinwheel Scheduling aims to find a perpetual schedule for unit-length tasks on a single machine subject to given maximal time spans (a.k.a. frequencies) between any two consecutive executions of the same task. The density of a Pinwheel Scheduling instance is the sum of the inverses of these task frequencies; the 5/6-Conjecture (Chan and Chin, 1993) states that any Pinwheel Scheduling instance with density at most 5/6 is schedulable. We formalize the notion of Pareto surfaces for Pinwheel Scheduling and exploit novel structural insights to engineer an efficient algorithm for computing them. This allows us to (1) confirm the 5/6-Conjecture for all Pinwheel Scheduling instances with at most 12 tasks and (2) to prove that a given list of only 23 schedules solves all schedulable Pinwheel Scheduling instances with at most 5 tasks.
PolyTrack: Tracking with Bounding Polygons

In this paper, we present a novel method called PolyTrack for fast multi-object tracking and segmentation using bounding polygons. Polytrack detects objects by producing heatmaps of their center keypoint. For each of them, a rough segmentation is done by computing a bounding polygon over each instance instead of the traditional bounding box. Tracking is done by taking two consecutive frames as input and computing a center offset for each object detected in the first frame to predict its location in the second frame. A Kalman filter is also applied to reduce the number of ID switches. Since our target application is automated driving systems, we apply our method on urban environment videos. We trained and evaluated PolyTrack on the MOTS and KITTIMOTS datasets. Results show that tracking polygons can be a good alternative to bounding box and mask tracking. The code of PolyTrack is available at https://github.com/gafaua/PolyTrack.
Arch-Net: Model Distillation for Architecture Agnostic Model Deployment

Vast requirement of computation power of Deep Neural Networks is a major hurdle to their real world applications. Many recent Application Specific Integrated Circuit (ASIC) chips feature dedicated hardware support for Neural Network Acceleration. However, as ASICs take multiple years to develop, they are inevitably out-paced by the latest development in Neural Architecture Research. For example, Transformer Networks do not have native support on many popular chips, and hence are difficult to deploy. In this paper, we propose Arch-Net, a family of Neural Networks made up of only operators efficiently supported across most architectures of ASICs. When a Arch-Net is produced, less common network constructs, like Layer Normalization and Embedding Layers, are eliminated in a progressive manner through label-free Blockwise Model Distillation, while performing sub-eight bit quantization at the same time to maximize performance. Empirical results on machine translation and image classification tasks confirm that we can transform latest developed Neural Architectures into fast running and as-accurate Arch-Net, ready for deployment on multiple mass-produced ASIC chips. The code will be available at https://github.com/megvii-research/Arch-Net.
Adjusting for misclassification of an exposure in an individual participant data meta‐analysis

A common problem in the analysis of multiple data sources, including individual participant data meta‐analysis (IPD‐MA), is the misclassification of binary variables. Misclassification may lead to biased estimators of model parameters, even when the misclassification is entirely random. We aimed to develop statistical methods that facilitate unbiased estimation of adjusted and unadjusted exposure‐outcome associations and between‐study heterogeneity in IPD‐MA, where the extent and nature of exposure misclassification may vary across studies. We present Bayesian methods that allow misclassification of binary exposure variables to depend on study‐ and participant‐level characteristics. In an example of the differential diagnosis of dengue using two variables, where the gold standard measurement for the exposure variable was unavailable for some studies which only measured a surrogate prone to misclassification, our methods yielded more accurate estimates than analyses naive with regard to misclassification or based on gold standard measurements alone. In a simulation study, the evaluated misclassification model yielded valid estimates of the exposure‐outcome association, and was more accurate than analyses restricted to gold standard measurements. Our proposed framework can appropriately account for the presence of binary exposure misclassification in IPD‐MA. It requires that some studies supply IPD for the surrogate and gold standard exposure, and allows misclassification to follow a random effects distribution across studies conditional on observed covariates (and outcome). The proposed methods are most beneficial when few large studies that measured the gold standard are available, and when misclassification is frequent.
Nearly Optimal Algorithms for Level Set Estimation

The level set estimation problem seeks to find all points in a domain ${\cal X}$ where the value of an unknown function $f:{\cal X}\rightarrow \mathbb{R}$ exceeds a threshold $\alpha$. The estimation is based on noisy function evaluations that may be acquired at sequentially and adaptively chosen locations in ${\cal X}$. The threshold value $\alpha$ can either be \emph{explicit} and provided a priori, or \emph{implicit} and defined relative to the optimal function value, i.e. $\alpha = (1-\epsilon)f(x_\ast)$ for a given $\epsilon>0$ where $f(x_\ast)$ is the maximal function value and is unknown. In this work we provide a new approach to the level set estimation problem by relating it to recent adaptive experimental design methods for linear bandits in the Reproducing Kernel Hilbert Space (RKHS) setting. We assume that $f$ can be approximated by a function in the RKHS up to an unknown misspecification and provide novel algorithms for both the implicit and explicit cases in this setting with strong theoretical guarantees. Moreover, in the linear (kernel) setting, we show that our bounds are nearly optimal, namely, our upper bounds match existing lower bounds for threshold linear bandits. To our knowledge this work provides the first instance-dependent, non-asymptotic upper bounds on sample complexity of level-set estimation that match information theoretic lower bounds.