Miracle-dz/UniFAR-FTUs huggingface dataset:
Predicting patients with false negative SARS-CoV-2 testing at hospital admission: A retrospective multi-center study

Importance: False negative SARS-CoV-2 tests can lead to spread of infection in the inpatient setting to other patients and healthcare workers. However, the population of patients with COVID who are admitted with false negative testing is unstudied. Objective: To characterize and develop a model to predict true SARS-CoV-2 infection among patients who initially test negative for COVID by PCR. Design: Retrospective cohort study. Setting: Five hospitals within the Yale New Haven Health System between 3/10/2020 and 9/1/2020. Participants: Adult patients who received diagnostic testing for SARS-CoV-2 virus within the first 96 hours of hospitalization. Exposure: We developed a logistic regression model from readily available electronic health record data to predict SARS-CoV-2 positivity in patients who were positive for COVID and those who were negative and never retested. Main Outcomes and Measures: This model was applied to patients testing negative for SARS-CoV-2 who were retested within the first 96 hours of hospitalization. We evaluated the ability of the model to discriminate between patients who would subsequently retest negative and those who would subsequently retest positive. Results: We included 31,459 hospitalized adult patients; 2,666 of these patients tested positive for COVID and 3,511 initially tested negative for COVID and were retested. Of the patients who were retested, 61 (1.7%) had a subsequent positive COVID test. The model showed that higher age, vital sign abnormalities, and lower white blood cell count served as strong predictors for COVID positivity in these patients. The model had moderate performance to predict which patients would retest positive with a test set area under the receiver-operator characteristic (ROC) of 0.76 (95% CI 0.70 - 0.83). Using a cutpoint for our risk prediction model at the 90th percentile for probability, we were able to capture 35/61 (57%) of the patients who would retest positive. This cutpoint amounts to a number-needed-to-retest range between 15 and 77 patients. Conclusion and Relevance: We show that a pragmatic model can predict which patients should be retested for COVID. Further research is required to determine if this risk model can be applied prospectively in hospitalized patients to prevent the spread of SARS-CoV-2 infections.
Deep Moment Matching Kernel for Multi-source Gaussian Processes.

Human learners have the ability to solve new tasks efficiently if previous knowledge is relevant, which has motivated research into few-shot learning and transfer learning. We formalize the integration of relevant knowledge as multi-source regression in which the target function is inferred using Gaussian Process (GP) with the deep moment matching (DMM) kernel. We obtain a non-stationary DMM kernel from prior relevant data by analytically calculating the covariance of the target function. We interpret the data-informed DMM kernel, which serves as prior for target function, as: (1) a refined similarity determined by squared distance in the latent space and (2) as propagating uncertainty measured in RKHS defined by the posterior covariance from the prior learning. In comparison with the autoregressive models, variational DGP models and others, results show GP regression with the DMM kernels is effective when applying to the standard synthetic and real-world multi-fidelity data sets.
1,3‐Dipolar Cycloadditions of α‐Diazo Ketones Derived from N‐Protected (S)‐Proline with Aromatic and Cycloaliphatic Thioketones

Enantiomerically pure alpha-oxo diazo compounds derived from (S)-proline were used for 1,3-dipolar cycloaddition with aryl and hetaryl thioketones, as well as with cycloalkanethiones. Whereas the reactions with hetaryl thioketones in boiling THF yield alpha,beta-unsaturated ketones via a cascade of cycloaddition, 1,3-dipolar electrocyclization, and desulfurization, the analogous reactions with thiobenzophenone and cycloalkanethiones result in the formation of 1,3-oxathiole derivatives. In the latter case, the 1,5-dipolar electrocyclization of the intermediate thiocarbonyl ylide is the key step of the reaction sequence. In all cases, the isolated products are optically active, i.e., the multistep processes occur with retention of the stereogenic center incorporated via the use of (S)-proline as the precursor of the diazo compounds.
Proof of new S-matrix formula from classical solutions in open string field theory (or, Deriving on-shell open string field amplitudes without using Feynman rules, Part II)

We study relation between the gauge invariant quantity obtained in [arXiv:1908.09784] and the Feynman diagrams in the dressed $ \mathcal B_0 $ gauge in the open cubic string field theory. We derive a set of recurrence relations that hold among the terms of this gauge invariant quantity. These relations are paraphrase of the $A_\infty$-relation which holds among Feynman diagrams. By using these relations, we prove that this gauge invariant quantity equals the $S$-matrix at the tree level. We also present a proof that a set of new Feynman rules proposed in [arXiv:2003.05021 [hep-th]] reproduces the onshell disc amplitudes correctly by using the same combinatorial identities.
Minimum ℓ 1 − norm interpolation via basis pursuit is robust to errors

: This article studies basis pursuit, i.e. minimum ℓ 1 -norm interpolation, in sparse linear regression with additive errors. No conditions on the errors are imposed. It is assumed that the number of i.i. d. Gaussian features grows superlinear in the number of samples. The main result is that under these conditions the Euclidean error of recovering the true regressor is of the order of the average noise level. Hence, the regressor recovered by basis pursuit is close to the truth if the average noise level is small. Lower bounds that show near optimality of the results complement the analysis. In addition, these results are extended to low rank trace regression. The proofs rely on new lower tail bounds for maxima of Gaussians vectors and the spectral norm of Gaussian matrices, respectively, and might be of independent interest as they are signiﬁcantly stronger than the corresponding upper tail bounds.
Learning Spatial Attention for Face Super-Resolution

General image super-resolution techniques have difficulties in recovering detailed face structures when applying to low resolution face images. Recent deep learning based methods tailored for face images have achieved improved performance by jointly trained with additional task such as face parsing and landmark prediction. However, multi-task learning requires extra manually labeled data. Besides, most of the existing works can only generate relatively low resolution face images (<italic>e.g</italic>., <inline-formula> <tex-math notation="LaTeX">$128\times 128$ </tex-math></inline-formula>), and their applications are therefore limited. In this paper, we introduce a novel SPatial Attention Residual Network (SPARNet) built on our newly proposed Face Attention Units (FAUs) for face super-resolution. Specifically, we introduce a spatial attention mechanism to the vanilla residual blocks. This enables the convolutional layers to adaptively bootstrap features related to the key face structures and pay less attention to those less feature-rich regions. This makes the training more effective and efficient as the key face structures only account for a very small portion of the face image. Visualization of the attention maps shows that our spatial attention network can capture the key face structures well even for very low resolution faces (<italic>e.g</italic>., <inline-formula> <tex-math notation="LaTeX">$16\times 16$ </tex-math></inline-formula>). Quantitative comparisons on various kinds of metrics (including PSNR, SSIM, identity similarity, and landmark detection) demonstrate the superiority of our method over current state-of-the-arts. We further extend SPARNet with multi-scale discriminators, named as SPARNetHD, to produce high resolution results (<italic>i.e</italic>., <inline-formula> <tex-math notation="LaTeX">$512\times 512$ </tex-math></inline-formula>). We show that SPARNetHD trained with synthetic data can not only produce high quality and high resolution outputs for synthetically degraded face images, but also show good generalization ability to real world low quality face images. Codes are available at <uri>https://github.com/chaofengc/Face-SPARNet</uri>.
Evaluating Explanations: How Much Do Explanations from the Teacher Aid Students?

While many methods purport to explain predictions by highlighting salient features, what aims these explanations serve and how they ought to be evaluated often go unstated. In this work, we introduce a framework to quantify the value of explanations via the accuracy gains that they confer on a student model trained to simulate a teacher model. Crucially, the explanations are available to the student during training, but are not available at test time. Compared with prior proposals, our approach is less easily gamed, enabling principled, automatic, model-agnostic evaluation of attributions. Using our framework, we compare numerous attribution methods for text classification and question answering, and observe quantitative differences that are consistent (to a moderate to high degree) across different student model architectures and learning strategies.1
Waveforms and End-to-End Efficiency in RF Wireless Power Transfer Using Digital Radio Transmitter

We study radio frequency (RF) wireless power transfer (WPT) using a digital radio transmitter for applications where alternative analog transmit circuits are impractical. An important parameter for assessing the viability of an RF WPT system is its end-to-end efficiency. In this regard, we present a prototype test bed comprising a software-defined radio (SDR) transmitter and an energy-harvesting receiver with a low resistive load; employing an SDR makes our research meaningful for simultaneous wireless information and power transfer (SWIPT). We analyze the effect of clipping and nonlinear amplification at the SDR on multisine waveforms. Our experiments suggest that when the direct current (dc) input power at the transmitter is constant, high peak-to-average power ratio (PAPR) multisines are unsuitable for RF WPT over a flat-fading channel, due to their low average radiated power. The results indicate that the end-to-end efficiency is positively correlated with the average RF power of the waveform and that it reduces with increasing PAPR. Consequently, digital modulations, such as phase shift keying (PSK) and quadrature amplitude modulation (QAM), yield better end-to-end efficiency than multisines. Moreover, the end-to-end efficiency of PSK and QAM signals is invariant to the transmission bit rate. An in-depth analysis of the end-to-end efficiency of WPT reveals that the transmitter efficiency is lower than the receiver efficiency. Furthermore, we study the impact of a reflecting surface on the end-to-end efficiency of WPT and assess the transmission quality of the information signals by evaluating their error vector magnitude (EVM) for SWIPT. Overall, the experimental observations of end-to-end efficiency and EVM suggest that, while employing an SDR transmitter with fixed dc input power, a baseband quadrature PSK signal is most suitable for SWIPT at large, among PSK and QAM signals.
VisEvol: Visual Analytics to Support Hyperparameter Search through Evolutionary Optimization

During the training phase of machine learning (ML) models, it is usually necessary to configure several hyperparameters. This process is computationally intensive and requires an extensive search to infer the best hyperparameter set for the given problem. The challenge is exacerbated by the fact that most ML models are complex internally, and training involves trial‐and‐error processes that could remarkably affect the predictive result. Moreover, each hyperparameter of an ML algorithm is potentially intertwined with the others, and changing it might result in unforeseeable impacts on the remaining hyperparameters. Evolutionary optimization is a promising method to try and address those issues. According to this method, performant models are stored, while the remainder are improved through crossover and mutation processes inspired by genetic algorithms. We present VisEvol, a visual analytics tool that supports interactive exploration of hyperparameters and intervention in this evolutionary procedure. In summary, our proposed tool helps the user to generate new models through evolution and eventually explore powerful hyperparameter combinations in diverse regions of the extensive hyperparameter space. The outcome is a voting ensemble (with equal rights) that boosts the final predictive performance. The utility and applicability of VisEvol are demonstrated with two use cases and interviews with ML experts who evaluated the effectiveness of the tool.
Narrowband Observations of Comet 46P/Wirtanen during Its Exceptional Apparition of 2018/19. I. Apparent Rotation Period and Outbursts

We obtained broad- and narrowband images of the hyperactive comet 46P/Wirtanen on 33 nights during its 2018/2019 apparition, when the comet made a historic close approach to the Earth. With our extensive coverage, we investigated the temporal behavior of the comet on both seasonal and rotational timescales. We used CN observations to explore the coma morphology, revealing that there are two primary active areas that produce spiral structures. The direction of rotation of these structures changes from pre- to postperihelion, indicating that the Earth crossed the comet’s equatorial plane sometime around perihelion. We also used the CN images to create photometric light curves that consistently show two peaks in the activity, confirming the two source regions. We measured the nucleus’s apparent rotation period at a number of epochs using both the morphology and the light curves. These results all show that the rotation period is continuously changing throughout our observation window, increasing from 8.98 hr in early November to 9.14 hr around perihelion and then decreasing again to 8.94 hr in February. Although the geometry changes rapidly around perihelion, the period changes cannot be primarily due to synodic effects. The repetition of structures in the coma, both within a night and from night to night, strongly suggests that the nucleus is in a near-simple rotation state. We also detected two outbursts, one on December 12 and the other on January 28. Using the apparent velocities of the ejecta in these events, 68 ± 5 and 162 ± 15 m s−1, respectively, we derived start times of 2018 December 12 at 00:13 UT ± 7 minutes and 2019 January 27 at 20:01 UT ± 30 minutes.
A KMOS survey of the nuclear disk of the Milky Way

Context. In the central few degrees of the bulge of the Milky Way there is a flattened structure of gas, dust, and stars, known as the central molecular zone, that is similar to nuclear disks in other galaxies. As a result of extreme foreground extinction, we possess only sparse information about the (mostly old) stellar population of the nuclear disk. Aims. In this work we present our KMOS spectroscopic survey of the stars in the nuclear disk reaching the old populations. To obtain an unbiased data set, we sampled stars in the full extinction range along each line of sight. Methods. We also observed reference fields in neighboring regions of the Galactic bulge. We describe the design and execution of the survey and present first results. Results. We obtain spectra and five spectral indices of 3113 stars with a median S/N of 67 and measure radial velocities for 3051 stars. Of those, 2735 sources have sufficient S/N to estimate temperatures and metallicities from indices. Conclusions. We derive metallicities using the CO 2-0 and Na I K-band spectral features, where we derive our own empirical calibration using metallicities obtained with higher-resolution observations. We use 183 giant stars for calibration spanning in metallicity from −2.5 to 0.6 dex and covering temperatures of up to 5500 K. The derived index based metallicities deviate from the calibration values with a scatter of 0.32 dex. The internal uncertainty of our metallicities is likely smaller. We use these metallicity measurements, together with the CO index, to derive effective temperatures using literature relations. We publish the catalog in this paper. Our data set complements Galactic surveys such as Gaia and APOGEE for the inner 200 pc radius of the Milky Way, which is not readily accessible by those surveys owing to extinction. We will use the derived properties in future papers for further analysis of the nuclear disk.
Labor Reforms in Rajasthan: A Boon or a Bane?

We examine the impact of labour law deregulations in the Indian state of Rajasthan on plant employment and productivity. In 2014, after a long time, Rajasthan was the first Indian state that introduced labour reforms in the Industrial Disputes Act (1947), the Factories Act (1948), the Contract Labour (Regulation and Abolition) Act (1970), and the Apprentices Act (1961). Exploiting this unique quasi-natural experiment, we apply a difference-in-difference framework using the Annual Survey of Industries panel data of manufacturing plants. Our results show that reforms had an unintended consequence of the decline in labour use. Also, worryingly, the flexibility resulted in the disproportionate decline in the directly employed worker. Evidence suggests that the reforms positively impacted the plants' value-added and productivity. The strength of these effects varies depending on the underlying industry and reform structure. These findings prove robust to a set of specifications.
AGN Triality of Triple Mergers: Multiwavelength Classifications

We present results from a multiwavelength analysis searching for multiple active galactic nucleus (AGN) systems in nearby (z < 0.077) triple galaxy mergers. Combining archival Chandra, Sloan Digital Data Survey (SDSS), Wide-field Infrared Survey Explorer (WISE), and Very Large Array observations, we quantify the rate of nearby triple AGNs, as well as investigate possible connections between supermassive black hole accretion and merger environments. Analyzing the multiwavelength observations of seven triple galaxy mergers, we find that one triple merger has a single AGN (NGC 3341); we discover, for the first time, four likely dual AGNs (SDSS J1027+1749, SDSS J1631+2352, SDSS J1708+2153, and SDSS J2356−1016); we confirm one triple-AGN system, SDSS J0849+1114; and one triple merger in our sample remains ambiguous (SDSS J0858+1822). Analyzing the WISE data, we find a trend of increasing N H (associated with the primary AGN) as a function of increasing W1 – W2 color, reflecting that the motions of gas and dust are coupled in merging environments, where large amounts of both can be funneled into the active central region during mergers. Additionally, we find that the one triple-AGN system in our sample has the highest levels of N H and W1 – W2 color, while the dual-AGN candidates all have lower levels; these results are consistent with theoretical merger simulations that suggest that higher levels of nuclear gas are more likely to activate AGNs in mergers.
Keplers's Equation and Angular Momentum: Historical Perspective, Critical Analysis and Implications for Development of the Orbital Mechanics/Dynamics, Mathematics and Physics

After some more than four centuries from the formulation and publication (in Astronomia Nova) of the Kepler's Equation, which relates the eccentric (and, intermediately, the true) anomaly of the planetary trajectories to the uniformly flowing time, in accordance with his Second ("Area") law, the subsequently -- in course of development of Orbital Mechanics -- to the 2${}^{nd}$ law related and formally derived non-existent (zero-valued) transverse acceleration is refuted. Certain implications to Elliptic Integration, Symplectic Integration, Symplectic Geometry/Topology, as well as the connection between physical and mathematical continua in the context of the multi-level, scale-invariant mechanics/dynamics (with the augmented central and torquing forces) are also briefly hinted to.
Morphological evolution of supermassive black hole merger hosts and multimessenger signatures

With the expectation that projects like LISA and Pulsar Timing Arrays will detect gravitational waves from supermassive black hole mergers in the near future, it is key that we understand what we can expect those detections to be, and maximize what we can learn from them. To address this, we study the mergers of supermassive black holes in the Illustris simulation, looking at both the overall rate of mergers, and the correlation between merging black holes and the galaxies which host them. We find that these mergers take place in typical galaxies along the $M_{\rm{BH}}-M_*$ relation, and that between LISA and PTA detections we can expect to probe the full range of galaxy masses. As galaxy mergers can trigger increased star formation, we do find that galaxies hosting low-mass black hole mergers tend to show a slight increase in star formation rates compared to a mass-matched sample. However, high-mass merger hosts have typical star formation rates, due to a combination of low gas fractions and powerful AGN feedback. Although minor black hole mergers do not correlate with disturbed morphologies, major mergers (especially between high-mass black holes) tend to show morphological evidence of a recent galaxy merger which survives for ~500 Myr. However, this typical timescale is of the same order of magnitude as the infall/hardening time of the merging black holes, suggesting that electromagnetic followups to gravitational wave signals may not be able to observe this correlation. We further find that incorporating a realistic timescale delay for the black hole mergers could shift the distribution of merger masses toward higher-mass mergers, decreasing the rate of LISA detections while increasing the rate of PTA detections.
IBM Employee Attrition Analysis

In this paper, we analyzed the dataset IBM Employee Attrition to find the main reasons why employees choose to resign. Firstly, we utilized the correlation matrix to see some features that were not significantly correlated with other attributes and removed them from our dataset. Secondly, we selected important features by exploiting Random Forest, finding monthlyincome, age, and the number of companies worked significantly impacted employee attrition. Next, we also classified people into two clusters by using K-means Clustering. Finally, We performed binary logistic regression quantitative analysis: the attrition of people who traveled frequently was 2.4 times higher than that of people who rarely traveled. And we also found that employees who work in Human Resource have a higher tendency to leave.
StructFormer: Joint Unsupervised Induction of Dependency and Constituency Structure from Masked Language Modeling

There are two major classes of natural language grammars — the dependency grammar that models one-to-one correspondences between words and the constituency grammar that models the assembly of one or several corresponded words. While previous unsupervised parsing methods mostly focus on only inducing one class of grammars, we introduce a novel model, StructFormer, that can induce dependency and constituency structure at the same time. To achieve this, we propose a new parsing framework that can jointly generate a constituency tree and dependency graph. Then we integrate the induced dependency relations into the transformer, in a differentiable manner, through a novel dependency-constrained self-attention mechanism. Experimental results show that our model can achieve strong results on unsupervised constituency parsing, unsupervised dependency parsing, and masked language modeling at the same time.