Miracle-dz/UniFAR-FTUs huggingface dataset:
National findings from the Tracks survey of people who inject drugs in Canada, Phase 4, 2017-2019.

Background The Tracks survey of people who inject drugs (PWID) collected data in 14 sentinel sites across Canada (2017-2019). Objective To describe the prevalence of human immunodeficiency virus (HIV) and hepatitis C and associated risk behaviours and to examine trends over time. Methods Information regarding socio-demographics, social determinants of health, use of prevention services and testing, drug use, risk behaviours, and HIV and hepatitis C testing, care and treatment was collected through interviewer-administered questionnaires. Biological samples were tested for HIV, hepatitis C antibodies and hepatitis C ribonucleic acid (RNA). Descriptive statistics were calculated and trends over time were assessed. Results Of the 2,383 participants, 65.6% were cisgender male, 42.2% were Indigenous, 48.0% completed some high school or less, 62.6% lived in unstable housing and 75.7% had ever been incarcerated. Average age was 40.1 years. The majority experienced stigma and discrimination (88.7%) and physical, sexual and/or emotional abuse in childhood (85.0%) or with a sexual partner (75.9%). The majority reported use of a needle/syringe distribution program (90.1%) and tested for HIV (90.5%) and hepatitis C (90.9%). Among participants who had ever had sex, the majority (59.2%) reported inconsistent condom use during vaginal and/or anal sex with a casual sex partner. Prevalence of HIV was 10.3% (82.9% were aware of infection status) and many (36.9%) were hepatitis C RNA-positive (50.1% were aware of infection status). Most surveillance indicators remained relatively stable from Phase 1 to Phase 4. Changes were found in substances used, and improvements were noted related to HIV and hepatitis C prevalence and care cascade indicators. Conclusion Many PWID in Canada were living in unstable housing and experienced high levels of stigma and discrimination. Prevalence of HIV and hepatitis C was high in some areas. These findings contribute to the evidence base used to inform targeted prevention and control measures.
Anomaly Detection of Elderly Patient Activities in Smart Homes using a Graph-Based Approach

Sensor-based smart home provide the ability to track resident activities without interfering in their daily routine. It is useful to detect and predict the behaviors of an elderly resident in order to improve the safety of the residents’ home environment and provide aid for their caregiver. This paper presents a graph-based approach that successfully discover patterns and anomalies in resident activities. We analyze activity graphs constructed from smart home daily activities to detect normative patterns as well as temporal, spatial, and behavioral anomalies. We also present case studies for cognitively impaired participants and discuss how these anomalies can be linked to the decline in their cognitive abilities which will ultimately provide clinicians and care givers important knowledge regarding their patients.
Independent innovation analysis for nonlinear vector autoregressive process

The nonlinear vector autoregressive (NVAR) model provides an appealing framework to analyze multivariate time series obtained from a nonlinear dynamical system. However, the innovation (or error), which plays a key role by driving the dynamics, is almost always assumed to be additive. Additivity greatly limits the generality of the model, hindering analysis of general NVAR process which have nonlinear interactions between the innovations. Here, we propose a new general framework called independent innovation analysis (IIA), which estimates the innovations from completely general NVAR. We assume mutual independence of the innovations as well as their modulation by a fully observable auxiliary variable (which is often taken as the time index and simply interpreted as nonstationarity). We show that IIA guarantees the identifiability of the innovations with arbitrary nonlinearities, up to a permutation and component-wise invertible nonlinearities. We propose two practical estimation methods, both of which can be easily implemented by ordinary neural network training. We thus provide the first rigorous identifiability result for general NVAR, as well as very general tools for learning such models.
An operator view of policy gradient methods

We cast policy gradient methods as the repeated application of two operators: a policy improvement operator $\mathcal{I}$, which maps any policy $\pi$ to a better one $\mathcal{I}\pi$, and a projection operator $\mathcal{P}$, which finds the best approximation of $\mathcal{I}\pi$ in the set of realizable policies. We use this framework to introduce operator-based versions of traditional policy gradient methods such as REINFORCE and PPO, which leads to a better understanding of their original counterparts. We also use the understanding we develop of the role of $\mathcal{I}$ and $\mathcal{P}$ to propose a new global lower bound of the expected return. This new perspective allows us to further bridge the gap between policy-based and value-based methods, showing how REINFORCE and the Bellman optimality operator, for example, can be seen as two sides of the same coin.
Improved short-baseline neutrino oscillation search and energy spectrum measurement with the PROSPECT experiment at HFIR

We present a detailed report on sterile neutrino oscillation and U-235 antineutrino energy spectrum measurement results from the PROSPECT experiment at the highly enriched High Flux Isotope Reactor (HFIR) at Oak Ridge National Laboratory. In 96 calendar days of data taken at an average baseline distance of 7.9 m from the center of the 85 MW HFIR core, the PROSPECT detector has observed more than 50,000 interactions of antineutrinos produced in beta decays of U-235 fission products. New limits on the oscillation of antineutrinos to light sterile neutrinos have been set by comparing the detected energy spectra of ten reactor-detector baselines between 6.7 and 9.2 meters. Measured differences in energy spectra between baselines show no statistically significant indication of antineutrinos to sterile neutrino oscillation and disfavor the Reactor Antineutrino Anomaly best-fit point at the 2.5$\sigma$ confidence level. The reported U-235 antineutrino energy spectrum measurement shows excellent agreement with energy spectrum models generated via conversion of the measured U-235 beta spectrum, with a $\chi^2$/DOF of 31/31. PROSPECT is able to disfavor at 2.4$\sigma$ confidence level the hypothesis that U-235 antineutrinos are solely responsible for spectrum discrepancies between model and data obtained at commercial reactor cores. A data-model deviation in PROSPECT similar to that observed by commercial core experiments is preferred with respect to no observed deviation, at a 2.2$\sigma$ confidence level.
Wheat-based intercropping: a review.

The world’s population is increasing rapidly, and in order to feed it, one of the most attractive strategies is to increase productivity per unit area of available land or to increase the land area under production, which seems shrinking day by day. Therefore, to maximize land use and production, the ultimate goal of agriculture, namely yield, intercropping is an advanced agronomic technique that allows two or more crops to yield from the same area of land. Better utilization of resources and reduced weed competition minimize the risk of food shortages by enhancing yield stability. Several factors can affect intercropping: plant density, sowing time, the maturity of a crop, the selection of crop that is compatible with another as well as farmers’ and the region’s socioeconomic conditions. In intercropping, the land equivalent ratio (LER) is used to measure the productivity of land. Since wheat is the most important cereal around the world and is most suitable for intercropping, this review focuses on wheat-based intercropping.
PROGRESS OF IN-PLANE SHEAR FAULTS FROM SUB- RAYLEIGH UP TO COMPRESSIONAL WAVE RUPTURE SPEEDS

It is now known that in-plane shear faults (primarily strike-slip earthquakes) can not only exceed the shear wave speed of the medium, but can even reach the compressional wave speed, according to theoretical and numerical studies, laboratory experiments and seismic data analysis. However, steadystate calculations on singular cracks showed that speeds between the Rayleigh R v and shear wave S v speeds were not possible, due to the fact that in such cases there is negative energy flux into the fault edge from the surrounding medium. A steady-state singular crack would not absorb strain-energy but generate it (see Broberg (1999) for details). Andrews (1976) showed that even for non-singular 2-D inplane ruptures which start from rest and accelerate to some terminal velocity, such a forbidden zone (between R v and S v ) does exist. hhe existence of this forbidden zone in rupture speed has been supported for steady state cracks in analytical (Burridge et al., 1979) and for spontaneous rupture in numerical (Liu et al., 2008; Lu et al., 2009) studies. Recently Bizzarri and Das (2012) showed, using an unprecedented numerical resolution of the forbidden zone, that for the 3-D ruptures, where the in-plane (mode II) and the anti-plane (mode III) modes of propagation are mixed together, obeying the linear slip-weakening governing equation, the rupture front actually does pass through this forbidden zone very fast. Motivated by this result, we examine here the passage of the pure in-plane shear rupture from the sub-Rayleigh to the compressional wave speeds in 2-D, with a very accurate estimation of the rupture speed. hhis issue is interesting and important, as for very long strike-slip faults in the Earth’s crust the rupture becomes primarily pure mode II when the fault length becomes much larger than its width. One hundred and twenty numerical experiments are carried out to investigate the entire range of possible rupture speeds from crack initiation to the compressional wave speed. hhe parameter region where super-shear rupture propagation could occur has been thoroughly scrutinized. hhe geometry of the problem is shown in Figure 1. hhe elasto-dynamic problem is numerically solved by using the finite difference code, originally developed by Andrews (1973) and modified by Bizzarri et al. (2001). hhe linear slip-weakening friction law (Ida, 1972) adopted here is described by
Prioritizing documentation effort: Can we do better?

Code documentations are essential for software quality assurance, but due to time or economic pressures, code developers are often unable to write documents for all modules in a project. Recently, a supervised artificial neural network (ANN) approach is proposed to prioritize important modules for documentation effort. However, as a supervised approach, there is a need to use labeled training data to train the prediction model, which may not be easy to obtain in practice. Furthermore, it is unclear whether the ANN approach is generalizable, as it is only evaluated on several small data sets. In this paper, we propose an unsupervised approach based on PageRank to prioritize documentation effort. This approach identifies "important" modules only based on the dependence relationships between modules in a project. As a result, the PageRank approach does not need any training data to build the prediction model. In order to evaluate the effectiveness of the PageRank approach, we use six additional large data sets to conduct the experiments in addition to the same data sets collected from open-source projects as used in prior studies. The experimental results show that the PageRank approach is superior to the state-of-the-art ANN approach in prioritizing important modules for documentation effort. In particular, due to the simplicity and effectiveness, we advocate that the PageRank approach should be used as an easy-to-implement baseline in future research on documentation effort prioritization, and any new approach should be compared with it to demonstrate its effectiveness.
Parametric mode regression for bounded responses

We propose new parametric frameworks of regression analysis with the conditional mode of a bounded response as the focal point of interest. Covariate effects estimation and prediction based on the maximum likelihood method under two new classes of regression models are demonstrated. We also develop graphical and numerical diagnostic tools to detect various sources of model misspecification. Predictions based on different central tendency measures inferred using various regression models are compared using synthetic data in simulations. Finally, we conduct regression analysis for data from the Alzheimer's Disease Neuroimaging Initiative to demonstrate practical implementation of the proposed methods. Supporting Information that contain technical details and additional simulation and data analysis results are available online.
Isolation, Identification and Characterization of Nitrate Reducing Bacteria from the Estuarine Waters of Selected Sampling Stations of Rajakkamangalam Estuary

Nitrate reduction by bacteria is a crucial step in nitrogen cycle making the Nitrogen available for organisms. Nitrate reductase enzyme and the bacteria as a denitrifying organism find important in applications such as waste treatment, aquaculture pond restoration, to improve crop growth. Aimed at good nitrate reducers, bacteria were isolated from the estuarine waters of Rajakamangalam, Kanyakumari, Southwest Coast of India. A total of 19 dissimilar bacterial strains were isolated and screened for nitrate reducing activity. Four isolates showed significant nitrate reducing ability and are subsequently characterised phenotypically and genotypically. The 16S rRNA sequences of the isolates were deposited in the Genbank with their respective names as Marinobacter hydrocarbonoclasticus, Bacillus subtilis, Denitrovibrio acetiphilus, and Pseudomonas citronellolis.
Critical Fermi surfaces in generic dimensions arising from transverse gauge field interactions

We study critical Fermi surfaces in generic dimensions arising from coupling finite-density fermions with transverse gauge fields, by applying the dimensional regularization scheme developed in Phys. Rev. B 92, 035141 (2015). We consider the cases of $U(1)$ and $U(1)\times U(1)$ transverse gauge couplings, and extract the nature of the renormalization group (RG) flow fixed points as well as the critical scalings. Our analysis allows us to treat a critical Fermi surface of a generic dimension $m$ perturbatively in an expansion parameter $\epsilon =\left (2-m \right ) / \left (m+1 \right ). $ One of our key results is that although the two-loop corrections do not alter the existence of an RG flow fixed line for certain $U(1)\times U(1)$ theories, which was identified earlier for $m=1$ at one-loop order, the third-order diagrams do. However, this fixed line feature is also obtained for $m>1$, where the answer is one-loop exact due to UV/IR mixing.
Matrix Completion with Quantified Uncertainty through Low Rank Gaussian Copula

Modern large scale datasets are often plagued with missing entries; indeed, in the context of recommender system, most entries are missing. While a flurry of imputation algorithms are proposed, almost none can estimate the uncertainty of its imputations. This paper proposes a probabilistic and scalable framework for missing value imputation with quantified uncertainty. Our model, the Low Rank Gaussian Copula, augments a standard probabilistic model, Probabilistic Principal Component Analysis, with marginal transformations for each column that allow the model to better match the distribution of the data. It naturally handles Boolean, ordinal, and real-valued observations and quantifies the uncertainty in each imputation. The time required to fit the model scales linearly with the number of rows and the number of columns in the dataset. Empirical results show the method yields state-of-the-art imputation accuracy across a wide range of datasets, including those with high rank. Our uncertainty measure predicts imputation error well: entries with lower uncertainty do have lower imputation error (on average). Boolean and ordinal entries with the lowest uncertainty have almost zero error. Moreover, for real-valued data, the resulting confidence intervals are well-calibrated.
TEMPORAL PATTERNS OF FITNESS AND SURVIVAL IN THE WOOD THRUSH

Data from a 22-yr study of individually marked Wood Thrushes (Hylocichla mustelina) in a Delaware forest fragment were analyzed to determine temporal and demographic patterns of nesting success, recruitment of local young into the population (fitness), and adult return rates (survival). Clutch size, number of eggs incubated, and number of eggs hatched were greater in early nests than in late nests. Although no difference existed in the average number of fledglings produced from early and late nests, fledglings from early nests were more likely to be recruited as breeders in the local population. Pairs of yearling (SY) birds produced equal numbers of recruits per nest from early and late nests, whereas pairs of older (≥2-yr-old, ASY) birds produced more recruits from early nests. The probability of fledging at least one young tended to be greater for late nests, and the percentage of eggs that eventually fledged was also greater for late nests. All nests were most likely to fail in the laying stage than du.
Komparasi Algoritma Klasifikasi untuk dataset iris dengan rapid miner

Data mining merupakan ilmu yang menggunakan data lampau sebagai acuan untuk mendapatkan sebuah pengetahuan baru. Salah satu peran utama data mining adalah klasifikasi. Dalam klasifikasi data lampau dihitung dan dijadikan sebagai model atau aturan untuk menentukan kelas dari data baru. Banyak algoritma klasifikasi dikembangkan dalam beberapa tahun terakhir. Beberapa algoritma klasifikasi terbaik antara lain C4.5, SVM serta Naïve Bayes. Dalam penelitian ini dibandingkan performa dari ketiga algoritma tersebut. Dengan menggunakan tools rapid miner dan dataset iris dari uci repository didapatkan bahwa algoritma C4.5 memiliki performa terbaik dengan tingkat akurasi sebesar 98,67%. Sedangkan Naïve Bayes memiliki tingkat akurasi sebesar 96,00% dan SVM sebesar 91,33%. Ketiga algoritma tersebut termasuk dalam golongan best classification karena memiliki tingkat akurasi diatas 90,00%.
Personalized Inﬂuencer Detection: Topic and Exposure-Conformity Aware

Inﬂuence analysis has been the subject of intense investigation, particularly since the emergence of online social networks. While early studies have focused on identifying global inﬂuencers, recently the attention has been enlarged to consider inﬂuence from the most diverse perspectives; e.g., topic-based inﬂuence propagation has been extensively analyzed and several important results have been obtained. A perspective that has not received suﬃcient attention is the study of personalized inﬂuence analysis; personalized here means the capability of inﬂuencing a speciﬁc user. Guo et al. have identiﬁed this as an important problem, noting that other kinds of inﬂuencers (such as the global ones) are not necessarily good individual inﬂuencers. The goal of this paper is to ﬁll this gap by proposing a mechanism to identify personalized inﬂuencers. Unlike prior studies that heavily rely on topic similarities , we incorporate interaction histories, referred as exposure conformity , in learning inﬂuence propagation probabilities. We propose a random-walk algo-rithm to tackle the problem. We have empirically validated the eﬀectiveness of our algorithm by comparing it against several widely-used baseline techniques. Finally, our ﬁndings contribute additional insights in support of the need for personalized inﬂuence analysis. Our ﬁndings also show interesting properties with respect to inﬂuence depending on the position of the individuals we are personalizing on.
Wafer-scale epitaxial growth of single orientation WS2 monolayers on sapphire

Realization of wafer-scale single-crystal films of transition metal dichalcogenides (TMDs) such as tungsten sulfide requires epitaxial growth and coalescence of oriented domains to form a continuous monolayer. The domains must be oriented in the same crystallographic direction on the substrate to avoid the formation of metallic inversion domain boundaries (IDBs) which are a common feature of layered chalcogenides. Here we demonstrate fully-coalesced single orientation tungsten sulfide monolayers on 2-inch diameter c-plane sapphire by metalorganic chemical vapor deposition using a multi-step growth process. High growth temperatures and sulfur/metal ratios were required to reduce domain misorientation and achieve epitaxial tungsten sulfide monolayers with low in-plane rotational twist (0.09 deg). Transmission electron microscopy analysis reveals that the tungsten sulfide monolayers lack IDBs but instead have translational boundaries that arise when tungsten sulfide domains with slightly off-set lattices merge together. By adjusting the monolayer growth rate, the density of translational boundaries and bilayer coverage were significantly reduced. The preferred orientation of domains is attributed to the presence of steps on the sapphire surface coupled with growth conditions promote surface diffusion and oriented attachment. The transferred tungsten sulfide monolayers show neutral and charged exciton emission at 80K with negligible defect-related luminescence. Back-gated tungsten sulfide field effect transistors exhibited mobility of 16 cm2/Vs. The results demonstrate the potential of achieving wafer-scale TMD monolayers free of inversion domains with properties approaching that of exfoliated flakes.
Parameterized Correlation Clustering in Hypergraphs and Bipartite Graphs

Motivated by applications in community detection and dense subgraph discovery, we consider new clustering objectives in hypergraphs and bipartite graphs. These objectives are parameterized by one or more resolution parameters in order to enable diverse knowledge discovery in complex data. For both hypergraph and bipartite objectives, we identify relevant parameter regimes that are equivalent to existing objectives and share their (polynomial-time) approximation algorithms. We first show that our parameterized hypergraph correlation clustering objective is related to higher-order notions of normalized cut and modularity in hypergraphs. It is further amenable to approximation algorithms via hyperedge expansion techniques. Our parameterized bipartite correlation clustering objective generalizes standard unweighted bipartite correlation clustering, as well as the bicluster deletion problem. For a certain choice of parameters it is also related to our hypergraph objective. Although in general it is NP-hard, we highlight a parameter regime for the bipartite objective where the problem reduces to the bipartite matching problem and thus can be solved in polynomial time. For other parameter settings, we present several approximation algorithms using linear program rounding techniques. These results allow us to introduce the first constant-factor approximation for bicluster deletion, the task of removing a minimum number of edges to partition a bipartite graph into disjoint bi-cliques. In several experimental results, we highlight the flexibility of our framework and the diversity of results that can be obtained in different parameter settings. This includes clustering bipartite graphs across a range of parameters, detecting motif-rich clusters in an email network and a food web, and forming clusters of retail products in a product review hypergraph, that are highly correlated with known product categories.