Miracle-dz/UniFAR-FTUs huggingface dataset:
Expert Systems With Applications

Prompt tuning with pre-trained language models (PLM) has exhibited outstanding performance by reducing the gap between pre-training tasks and various downstream applications, which requires additional labor efforts in label word mappings and prompt template engineering. However, in a label intensive research domain, e.g., few-shot relation extraction (RE), manually defining label word mappings is particularly challenging, because the number of utilized relation label classes with complex relation names can be extremely large. Besides, the manual prompt development in natural language is subjective to individuals. To tackle these issues, we propose a virtual prompt pre-training method, projecting the virtual prompt to latent space, then fusing with PLM parameters. The pre-training is entity-relation-aware for RE, including the tasks of mask entity prediction, entity typing, distant supervised RE, and contrastive prompt pre-training. The proposed pre-training method can provide robust initialization for prompt encoding, while maintaining the interaction with the PLM. Furthermore, the virtual prompt can effectively avoid the labor efforts and the subjectivity issue in label word mapping and prompt template engineering. Our proposed prompt-based prototype network delivers a novel learning paradigm to model entities and relations via the probability distribution and Euclidean distance of the predictions of query instances and prototypes. The results indicate that our model yields an averaged accuracy gain of 4.21% on two few-shot datasets over strong RE baselines. Based on our proposed framework, our pre-trained model outperforms the strongest RE-related PLM by 6.52%.
The onset of star formation 250 million years after the Big Bang

Abstract In this IAU symposium, we present results of our recent paper, Hashimoto et al. (2018a) focusing on its spectral energy distribution modeling. We present spectroscopic observations of MACS1149-JD1, a gravitationally lensed galaxy originally discovered by Zheng et al. (2012) via the dropout technique. Using the Atacama Large Millimeter/submillimeter Array (ALMA), we detect an emission line of doubly ionized oxygen, [Oiii] 88 μm, at a redshift of 9.1096±0.0006. This precisely determined redshift indicates that the red rest-frame optical colour observed with the Spitzer Space Telescope arises from a dominant stellar component that formed about 250 million years after the Big Bang, corresponding to a redshift of about 15. MACS1149-JD1 clearly demonstrates the importance and power of spectral energy distribution modeling to understand the earliest star formation history of the Universe.
Beagle : Automated Extraction and Interpretation of Visualizations from the Web

In this paper, we present Beagle, an automated data collection system to mine the web for SVG-based visualization images, label them with their corresponding visualization type (i.e., bar, scatter, pie, etc.), and make them available as a queryable data store. The key idea behind Beagle is a new SVG-based classification design to more effectively label visualizations rendered in a browser. Furthermore, Beagle is designed from the ground up to be extendable and modifiable in a straightforward way, to anticipate when new artifacts appear on the web, such as new JavaScript libraries, new visualization types, and better browser support for SVG. We evaluated Beagle’s classification techniques on multiple collections of SVG-based visualizations extracted from the web, and found that Beagle provides a significant boost in accuracy compared to existing classification techniques, across a wide variety of visualization types.
Robust Asymmetric Clustering

Contaminated mixture models are developed for model-based clustering of data with asymmetric clusters as well as spurious points, outliers, and/or noise. Specifically, we introduce a contaminated mixture of contaminated shifted asymmetric Laplace distributions and a contaminated mixture of contaminated skew-normal distributions. In each case, mixture components have a parameter controlling the proportion of bad points (i.e., spurious points, outliers, and/or noise) and one specifying the degree of contamination. A very important feature of our approaches is that these parameters do not have to be specified a priori. Expectation-conditional maximization algorithms are outlined for parameter estimation and the number of components is selected using the Bayesian information criterion. The performance of our approaches is illustrated on artificial and real data.
Actively tunable double-Fano and Ramsey-Fano resonances in photonic molecules and improved sensing performance

Optical Fano resonances are an increasingly important line-shape engineering tool with applications ranging from high-sensitivity sensing and ultrasmall lasers to low-power optical switching or modulating. Here we demonstrate a fully on-chip resonant nanostructure on a photonic crystal molecule platform exhibiting typical double- and Ramsey-Fano resonances, which can be actively controlled by the modification of the blockade transmittance in the waveguide. First, we investigate the transmission spectrum of a coupled double-cavity setting, showing a kind of double-Fano resonance line shape which consists of an asymmetric low-frequency Fano (LF) resonance and a high-frequency Fano (HF) resonance. At the same time, we elucidate the influences of various physical quantities on the generated LF and HF resonances. Second, and more interestingly, we reveal the occurrence of the Ramsey-Fano resonance profile by extending a double-cavity arrangement to a coupled-cavity-array arrangement. This Ramsey-Fano resonance can be attributed to the multiple quantum interference among a variety of light pathways. Finally, as an application, we discuss how to use an asymmetric double-Fano resonance line shape, which features the steep spectral slope, to improve the sensing performance. Our obtained results may stimulate future experimental efforts in controlling the double- and Ramsey-Fano resonance line shapes of this system more accurately.
Towards reliable calcification detection: calibration of uncertainty in coronary optical coherence tomography images

: Optical coherence tomography (OCT) has become increasingly essential in assisting the treatment of coronary artery disease (CAD). Image-guided solutions such as Percutaneous Coronary Intervention (PCI) are extensively used during the treatment of CAD. However, unidentiﬁed calciﬁed regions within a narrowed artery could impair the outcome of the PCI. Prior to treatments, object detection is paramount to automatically procure accurate readings on the location and thickness of calciﬁcations within the artery. Deep learning-based object detection methods have been explored in a variety of applications. The quality of object detection predictions could lead to uncertain results, which are not desirable in safety-critical scenarios. In this work, we implement an object detection model, You-Only-Look-Once v5 (YOLO), on a calciﬁcation detection framework within coronary OCT images. We evaluate the uncertainty of predictions based on the expected calibration errors, thus assessing the certainty level of detection results. To calibrate conﬁdence scores of predictions, we implement dependent logistic calibration using each detection result’s conﬁdence and center coordinates. With the calibrated conﬁdence score of each prediction, we lower the uncertainty of predictions in calciﬁcation detection. Our results show that the YOLO achieves higher precision and recall in comparison with the other object detection model, meanwhile producing more reliable results. The calibrated conﬁdence of prediction results in a conﬁdence error of approximately 0.13
Quantiﬁcation of Daily-Living Gait Quantity and Quality Using a Wrist-Worn Accelerometer in Huntington’s Disease

Conclusions: Results suggest that a wrist-worn accelerometer can be used to evaluate the quantity and quality of daily-living gait in people with HD, while accounting for the inﬂuence of irregular (choreic-like) movements, and that gait features related to within-and across-bout consistency markedly differ in individuals with HD and non-HD peers.
Distribution estimation and change-point detection for time series via DNN-based GANs

A BSTRACT . The generative adversarial networks (GANs) have recently been applied to estimating the distribution of independent and identically distributed data, and got excellent performances. In this paper, we use the blocking technique to demonstrate the effectiveness of GANs for estimating the distribution of stationary time series. Theoretically, we obtain a non-asymptotic error bound for the Deep Neural Network (DNN)-based GANs estimator for the stationary distribution of the time series. Based on our theoretical analysis, we put forward an algorithm for detecting the change-point in time series. We simulate in our ﬁrst experiment a stationary time series by the multivariate autoregressive model to test our GAN estimator, while the second experiment is to use our proposed algorithm to detect the change-point in a time series sequence. Both perform very well. The third experiment is to use our GAN estimator to learn the distribution of a real ﬁnancial time series data, which is not stationary, we can see from the experiment results that our estimator cannot match the distribution of the time series very well but give the right changing tendency.
Properties of galaxies around the most massive SMBHs

Abstract We present result of the clustering analysis performed between AGNs and galaxies. AGN samples with redshift 0.1–1.0 were extracted from AGN properties catalogs which contain virial mass estimates of SMBHs. Galaxy samples were extracted from SDSS DR8 catalog and UKIDSS DR9 LAS catalog. The catalogs of SDSS and UKIDSS were merged and used to estimate the IR-opt color and IR magnitude in the rest frame by SED fitting. As we had no redshift information on the galaxy samples, stacking method was applied. We investigated the BH mass dependence of cross correlation length, red galaxy fraction at their environment, and luminosity function of galaxies. We found that the cross correlation length increase above M BH ≥ 108.2 M ⊙, and red galaxies dominate the environment of AGNs with M BH ≥ 109 M ⊙. This result indicates that the most massive SMBHs are mainly fueled by accretion of hot halo gas.
A Large-scale Industrial and Professional Occupation Dataset

There has been growing interest in utilizing occupational data mining and analysis. In today's job market, occupational data mining and analysis is growing in importance as it enables companies to predict employee turnover, model career trajectories, screen through resumes and perform other human resource tasks. A key requirement to facilitate these tasks is the need for an occupation-related dataset. However, most research use proprietary datasets or do not make their dataset publicly available, thus impeding development in this area. To solve this issue, we present the Industrial and Professional Occupation Dataset (IPOD), which comprises 192k job titles belonging to 56k LinkedIn users. In addition to making IPOD publicly available, we also: (i) manually annotate each job title with its associated level of seniority, domain of work and location; and (ii) provide embedding for job titles and discuss various use cases. This dataset is publicly available at this https URL.
Testing out suicide risk prediction algorithms using phone measurements with patients in acute mental health settings: a feasibility study

Background: Digital phenotyping and machine learning are nowadays being used to augment or even replace traditional analytic procedures in many domains, including health care. Given the heavy reliance on smartphones and mobile devices around the world, this readily available source of data is an important and highly underutilized source that has the potential to improve mental health risk prediction and prevention and advance global mental health. Objective: To apply machine learning in an acute mental health setting for suicide risk prediction. This study is using a nascent approach, adding to existing knowledge by using data collected through a smartphone in place of clinical data which has typically been collected from health care records. Methods: We created a smartphone application called Strength Within Me (SWiM) that was linked to Fitbit, Apple Health kit, and Facebook, to collect salient clinical information such as sleep behavior and mood, step frequency and count, and engagement patterns with the phone from a cohort of acute mental health inpatients (n=66). In addition, clinical research interviews were used to assess mood, sleep, and suicide risk. Multiple machine learning algorithms were tested to determine best fit. Results: K-Nearest Neighbors (k=2) with uniform weighting and Euclidean distance metric emerged as the most promising algorithm, with 68% average accuracy (averaged over 10,000 simulations of splitting the training and testing data via 10-fold cross validation) and average AUC of 0.65. We applied a 5x2cv combined F test to test model performance of KNN against baseline classifier that guesses training majority, random forest, and others and achieved F statistics of 10
Basket Completion with Multi-task Determinantal Point Processes

Determinantal point processes (DPPs) have received significant attention in the recent years as an elegant model for a variety of machine learning tasks, due to their ability to elegantly model set diversity and item quality or popularity. Recent work has shown that DPPs can be effective models for product recommendation and basket completion tasks. We present an enhanced DPP model that is specialized for the task of basket completion, the multi-task DPP. We view the basket completion problem as a multi-class classification problem, and leverage ideas from tensor factorization and multi-class classification to design the multi-task DPP model. We evaluate our model on several real-world datasets, and find that the multi-task DPP provides significantly better predictive quality than a number of state-of-the-art models.
Enhancement of DVB-S2 Network Stability and Performance for Mobile User Groups

Digital Video Broadcast-Satellite 2 (DVB-S2) is a second generation technology which is developed and standardized as an evolution of Digital Video Broadcast (DVB-S) by European Telecommunication Standard Institute (ETSI). This investigation is mainly based on the study and analysis of DVB-S2 networks for mobile users. The DVB-S revolutionizes broadcasting industry in terms of providing TV contents to sub-continents over satellite. While the DVB-S2 provides enhancements in broadcasting television contents such as high definition TV, which can be provided to viewers on a reduced cost. The DVB-S2 describes the methods in which the data is framed, encoded and modulated for transmission over a satellite channel. The DVB-S2 comes from the same family of DVB-S with robust features. Applications related to broadcast industry covered in DVB-S2 are High Definition TV (HDTV), SDTV, Digital Satellite NEWS Gathering (DSNG) and TV distribution to terrestrial transmitters. This study presented DVB-S2 capabilities of handling mobility issues specifically on mobile platforms including the simulation of Adaptive Coding Modulation (ACM), atmospheric effects and noisy channels. This study introduced ACM engine which allows tuning of satellite systems and analyzing system stability and performance. This study highlighted the importance for conducting further study in order to propose an appropriate solution to enhance the link stability under bad climate conditions.
Study of the role of the magnetic configuration in a k-ε model for anomalous transport in tokamaks.

A reduced model for the anomalous transport of magnetically confined plasma in the edge and SOL, inspired by the Reynolds-Averaged Navier-Stokes (RANS) approach, is presented assuming diffusion as governing mechanism. An empirical equation is built for the turbulent kinetic energy and the system is closed via the scaling of global confinement, the experimental dependencies to machine parameters being recovered for the SOL width. The model is implemented in the transport code SolEdge2D and tested with respect to three magnetic configurations of TCV. Profiles of density, temperature and perpendicular heat flux are then compared to experimental data. Particular attention is devoted to the effect of the magnetic configuration on the SOL width and particle transport coefficient to different configurations.
Virtual Borders: Online Nominal Rigidities and International Market Segmentation

Do prices respond to macro shocks? Does the mere presence of international frontiers hinder trade? We revisit these questions by studying a dataset of online book prices for a number of US and Canadian retailers. We believe our dataset is well suited to this task for a number of reasons: (1) data for multiple retailers are available; (2) the products sold are identical across retailers; (3) the sample spans a period of large fluctuations in the bilateral exchange rate; (4) the nature of the industry is such that physical distance is irrelevant beyond shipping costs which are observable; (5) nominal frictions in the form of menu costs are arguably minimal; and (6) proxies for sales are available for most retailers. Given the unique nature of our dataset, the first objective of the paper is to document the degree of price rigidity and price dispersion. Our main findings are: online book prices display significant stickiness; there is a large degree of heterogeneity across retailers in terms of price rigidity and pricing strategy; price dispersion is high both within and across borders. Also, price levels do not appear to respond to exchange rate fluctuations. Building on the predictions from a simple two-country, multi-firm model and by exploiting information contained both in prices and quantities, we show that market segmentation is probably behind this disconnect .
Directive electromagnetic radiation of a line source scattered by a PEMC cylinder coated with anisotropic media

ABSTRACT In this paper, the electromagnetic radiation of a line source scattered by an infinite perfect electromagnetic conductor (PEMC) coated with anisotropic media is analytically analyzed. A line source is placed inside the anisotropic media. To validate the presented formulas, the radiation patterns for isotropic cases are compared to the published literature and good agreement is found between them. Also, the directivity of the electromagnetic radiation pattern is improved using the anisotropic coating. Furthermore, when the line source is placed on the surface of PEMC core and the relative tensors of anisotropic shell are appropriately chosen, the co- and cross-polarization components of the fields form the same radiation patterns.
Dimensionalityreduction output b Observe Modify a input Dimensionalityreduction output a ObserveModify

Dimensionality reduction is a common method for analyzing and visualizing high-dimensional data. However, reasoning dynamically about the results of a dimensionality reduction is difficult. Dimensionality-reduction algorithms use complex optimizations to reduce the number of dimensions of a dataset, but these new dimensions often lack a clear relation to the initial data dimensions, thus making them difficult to interpret. Here we propose a visual interaction framework to improve dimensionality-reduction based exploratory data analysis. We introduce two interaction techniques, forward projection and backward projection, for dynamically reasoning about dimensionally reduced data. We also contribute two visualization techniques, prolines and feasibility maps, to facilitate the effective use of the proposed interactions. We apply our framework to PCA and autoencoder-based dimensionality reductions. Through data-exploration examples, we demonstrate how our visual interactions can improve the use of dimensionality reduction in exploratory data analysis.
Validation of a steady BEM-FEM coupled simulation with experiments on flexible small scale propellers

This paper presents numerical and experimental results for the validation of a coupled Boundary Element Method (BEM)-Finite Element Method (FEM) simulation for uniform flows. In the first part of the paper the numerical models and the BEM-FEM coupling are presented. A large part of the paper is dedicated to the validation procedure which has been successfully applied to three small scale flexible propellers. Special attention has been paid to improve the accuracy of blade deformation measurements in a cavitation tunnel test setup. Results of tests on a flat plate show that accurate deformation measurements can be performed with a stereo camera system in combination with a Digital Image Correlation (DIC) technique. In the last part of the paper blade deformations calculated with the BEM-FEM coupling are compared to experimental results. Depending on the flow condition a good agreement between measured and calculated blade deformations was obtained.
The Symmetric Convex Ordering: A Novel Partial Order for B-DMCs Ordering the Information Sets of Polar Codes

In this paper, we propose a novel partial order for binary discrete memoryless channels that we call the symmetric convex ordering. We show that Ar{\i}kan's polar transform preserves 'symmetric convex orders'. Furthermore, we show that while for symmetric channels this ordering turns out to be equivalent to the stochastic degradation ordering already known to order the information sets of polar codes, a strictly weaker partial order is obtained when at least one of the channels is asymmetric. In between, we also discuss two tools which can be useful for verifying this ordering: a criterion known as the cut criterion and channel symmetrization. Finally, we discuss potential applications of the results to polar coding over non-stationary channels.
Interventions to Improving Osteoporosis Screening: An Iowa Research Network (IRENE) Study

Background: Primary care physicians often fail to diagnose low bone density. This pilot study assessed 2 interventions for their effect on bone mineral density testing. Methods: Five practices in the Iowa Research Network were randomized: 2 to chart reminder alone (CR), 2 to chart reminder plus mailed patient education (CR+PtEd), and one to usual care. A total of 204 women aged 65 years or older were recruited from within these practices. Bayesian hierarchical analyses were used instead of traditional statistical methods to take advantage of collateral data and to adjust for differences between clinics at baseline. Results: After the intervention, the rates of completed bone mineral density testing were 45.2% in the CR+PtEd group, 31.4% in the chart remainder only group, and 9.7% in the usual care practice. Bayesian analysis adjusted for patient and clinic characteristics, which made use of collateral data, gave an odds ratio of 5.47 for the effect of CR+PtEd group. The Bayesian P was . 029 and the one-sided 95% credible interval for the odds ratio was greater than 1.2. The effect of CR+PtEd was confirmed by sensitivity analyses. Traditional hierarchical analysis adjusted for practice characteristics could not be used to estimate statistical significance because there were not enough clinics to accommodate a model that included all the important covariables. Conclusions: Specific chart reminders to physicians combined with mailed patient education substantially increased the levels of bone density testing and could potentially be used to improve osteoporosis screening in primary care. Bayesian hierarchical analysis makes it possible to assess practice-level interventions when few practices are randomized.