Miracle-dz/UniFAR-FTUs huggingface dataset:
PubMedQA: A Dataset for Biomedical Research Question Answering

We introduce PubMedQA, a novel biomedical question answering (QA) dataset collected from PubMed abstracts. The task of PubMedQA is to answer research questions with yes/no/maybe (e.g.: Do preoperative statins reduce atrial fibrillation after coronary artery bypass grafting? ) using the corresponding abstracts. PubMedQA has 1k expert-annotated, 61.2k unlabeled and 211.3k artificially generated QA instances. Each PubMedQA instance is composed of (1) a question which is either an existing research article title or derived from one, (2) a context which is the corresponding abstract without its conclusion, (3) a long answer, which is the conclusion of the abstract and, presumably, answers the research question, and (4) a yes/no/maybe answer which summarizes the conclusion. PubMedQA is the first QA dataset where reasoning over biomedical research texts, especially their quantitative contents, is required to answer the questions. Our best performing model, multi-phase fine-tuning of BioBERT with long answer bag-of-word statistics as additional supervision, achieves 68.1% accuracy, compared to single human performance of 78.0% accuracy and majority-baseline of 55.2% accuracy, leaving much room for improvement. PubMedQA is publicly available at https://pubmedqa.github. io.
BPnP: Further Empowering End-to-End Learning with Back-Propagatable Geometric Optimization

In this paper we present BPnP, a novel method to do back-propagation through a PnP solver. We show that the gradients of such geometric optimization process can be computed using the Implicit Function Theorem as if it is differentiable. Furthermore, we develop a residual-conformity trick to make end-to-end pose regression using BPnP smooth and stable. We also propose a "march in formation" algorithm which successfully uses BPnP for keypoint regression. Our invention opens a door to vast possibilities. The ability to incorporate geometric optimization in end-to-end learning will greatly boost its power and promote innovations in various computer vision tasks.
Scene Graph Parsing by Attention Graph

Scene graph representations, which form a graph of visual object nodes together with their attributes and relations, have proved useful across a variety of vision and language applications. Recent work in the area has used Natural Language Processing dependency tree methods to automatically build scene graphs. In this work, we present an 'Attention Graph' mechanism that can be trained end-to-end, and produces a scene graph structure that can be lifted directly from the top layer of a standard Transformer model. The scene graphs generated by our model achieve an F-score similarity of 52.21% to ground-truth graphs on the evaluation set using the SPICE metric, surpassing the best previous approaches by 2.5%.
Energy-Based Continuous Inverse Optimal Control

The problem of continuous inverse optimal control (over finite time horizon) is to learn the unknown cost function over the sequence of continuous control variables from expert demonstrations. In this article, we study this fundamental problem in the framework of energy-based model (EBM), where the observed expert trajectories are assumed to be random samples from a probability density function defined as the exponential of the negative cost function up to a normalizing constant. The parameters of the cost function are learned by maximum likelihood via an “analysis by synthesis” scheme, which iterates: 1) synthesis step: sample the synthesized trajectories from the current probability density using the Langevin dynamics via backpropagation through time and 2) analysis step: update the model parameters based on the statistical difference between the synthesized trajectories and the observed trajectories. Given the fact that an efficient optimization algorithm is usually available for an optimal control problem, we also consider a convenient approximation of the above learning method, where we replace the sampling in the synthesis step by optimization. Moreover, to make the sampling or optimization more efficient, we propose to train the EBM simultaneously with a top-down trajectory generator via cooperative learning, where the trajectory generator is used to fast initialize the synthesis step of the EBM. We demonstrate the proposed methods on autonomous driving tasks and show that they can learn suitable cost functions for optimal control.
Modelling Stopping Criteria for Search Results using Poisson Processes

Text retrieval systems often return large sets of documents, particularly when applied to large collections. Stopping criteria can reduce the number of these documents that need to be manually evaluated for relevance by predicting when a suitable level of recall has been achieved. In this work, a novel method for determining a stopping criterion is proposed that models the rate at which relevant documents occur using a Poisson process. This method allows a user to specify both a minimum desired level of recall to achieve and a desired probability of having achieved it. We evaluate our method on a public dataset and compare it with previous techniques for determining stopping criteria.
The first 3D Morpho-kinematical model of a Supernova Remnant: The case of VRO 42.05.01 (G 166.0+4.3)

We present the preliminary results of the first 3-dimensional (3D) Morpho-Kinematical (MK) model of a supernova remnant (SNR), using as a study case the Galactic SNRVRO 42.05. 01 (G 166.0+4.3). For the purpose of our modelling, the astrophysical software SHAPE was employed in which wide field imaging and high resolution spectroscopic data were utilized. We found that the remnant is consisted by three basic distinctive components: a “shell”, a “wing” and a “hat”, which present different morphological and kinematical behaviour, probably due to different ambient medium properties. The whole nebula has an inclination of 6− 8 degrees with respect to the plane of the sky and a systemic velocity Vsys ∼ -15 to -25 km s−1. Finally, we discuss the possible implications of our model’s results on the origin and evolution of VRO 42.05. 01.
The likelihood of detecting young giant planets with high-contrast imaging and interferometry

Giant planets are expected to form at orbital radii that are relatively large compared to transit and radial velocity detections (>1 au). As a result, giant planet formation is best observed through direct imaging. By simulating the formation of giant (0.3–5MJ) planets by core accretion, we predict planet magnitude in the near-infrared (2–4 μm) and demonstrate that, once a planet reaches the runaway accretion phase, it is self-luminous and is bright enough to be detected in near-infrared wavelengths. Using planet distribution models consistent with existing radial velocity and imaging constraints, we simulate a large sample of systems with the same stellar and disc properties to determine how many planets can be detected. We find that current large (8–10 m) telescopes have at most a 0.2 per cent chance of detecting a core-accretion giant planet in the L’ band and 2 per cent in the K band for a typical solar-type star. Future instruments such as METIS and VIKiNG have higher sensitivity and are expected to detect exoplanets at a maximum rate of 2 and 8 per cent, respectively.
Conformal Positive Mass Theorems for Manifolds with Charge.

In this paper, we prove conformal positive mass theorems for asymptotically flat manifolds with charge. We apply conformal relations to show that if the conformal sum of scalar curvature is not less than the norm square of electric field and electric density, the sum of the mass will not less than the modulus of total electric charge. We also study the situation with inner boundary condition and manifolds with scalar charge.
Debreach: Mitigating Compression Side Channels via Static Analysis and Transformation

Compression is an emerging source of exploitable side-channel leakage that threatens data security, particularly in web applications where compression is indispensable for performance reasons. Current approaches to mitigating compression side channels have drawbacks in that they either degrade compression ratio drastically or require too much effort from developers to be widely adopted. To bridge the gap, we develop Debreach, a static analysis and program transformation based approach to mitigating compression side channels. Debreach consists of two steps. First, it uses taint analysis to soundly identify flows of sensitive data in the program and uses code instrumentation to annotate data before feeding them to the compressor. Second, it enhances the compressor to exploit the freedom to not compress of standard compression protocols, thus removing the dependency between sensitive data and the size of the compressor's output. Since Debreach automatically instruments applications and does not change the compression protocols, it has the advantage of being non-disruptive and compatible with existing systems. We have evaluated Debreach on a set of web server applications written in PHP. Our experiments show that, while ensuring leakage-freedom, Debreach can achieve significantly higher compression performance than state-of-the-art approaches.
Deep Joint Embeddings of Context and Content for Recommendation

This paper proposes a deep learning-based method for learning joint context-content embeddings (JCCE) with a view to context-aware recommendations, and demonstrate its application in the television domain. JCCE builds on recent progress within latent representations for recommendation and deep metric learning. The model effectively groups viewing situations and associated consumed content, based on supervision from 2.7 million viewing events. Experiments confirm the recommendation ability of JCCE, achieving improvements when compared to state-of-the-art methods. Furthermore, the approach shows meaningful structures in the learned representations that can be used to gain valuable insights of underlying factors in the relationship between contextual settings and content properties.
CoCoS: Fast and Accurate Distributed Triangle Counting in Graph Streams

Given a graph stream, how can we estimate the number of triangles in it using multiple machines with limited storage? Specifically, how should edges be processed and sampled across the machines for rapid and accurate estimation? The count of triangles (i.e., cliques of size three) has proven useful in numerous applications, including anomaly detection, community detection, and link recommendation. For triangle counting in large and dynamic graphs, recent work has focused largely on streaming algorithms and distributed algorithms but little on their combinations for “the best of both worlds. ” In this work, we propose CoCoS, a fast and accurate distributed streaming algorithm for estimating the counts of global triangles (i.e., all triangles) and local triangles incident to each node. Making one pass over the input stream, CoCoS carefully processes and stores the edges across multiple machines so that the redundant use of computational and storage resources is minimized. Compared to baselines, CoCoS is: (a) accurate: giving up to smaller estimation error; (b) fast: up to faster, scaling linearly with the size of the input stream; and (c) theoretically sound: yielding unbiased estimates.
Semi-analytic velocity profile for a Bingham fluid in a curved channel.

We derive an expression for the velocity profile of a pressure-driven yield-stress Bingham fluid flowing around a 2D concentric annulus. The formula requires the numerical solution of a nonlinear equation for the positions of the yield surfaces. The results allow the prediction of the effects of channel curvature on the pressure gradient required to initiate flow for given yield stress, and for the width of the plug region and the flux through the channel at different curvatures.
Radio Emission from Supernovae in the Very Early Phase: Implications for the Dynamical Mass Loss of Massive Stars

Recent high-cadence transient surveys and rapid follow-up observations indicate that some massive stars may dynamically lose their own mass within decades before supernovae (SNe). Such a mass-loss forms “confined” circumstellar medium (CSM); a high-density material distributed only within a small radius (≲1015 cm with the mass-loss rate of 0.01 ∼ 10−4 M⊙ yr−1). While the SN shock should trigger particle acceleration and magnetic field amplification in the “confined” CSM, synchrotron emission may be masked in centimeter wavelengths due to free–free absorption; the millimeter range can, however, be a potential new window. We investigate the time evolution of synchrotron radiation from the system of a red supergiant surrounded by the “confined” CSM, relevant to typical Type II-P SNe. We show that synchrotron millimeter emission is generally detectable, and that the signal can be used as a sensitive tracer of the nature of the “confined” CSM; it traces different CSM density parameter space than in the optical. Furthermore, our simulations show that the “confined” CSM efficiently produces secondary electrons and positrons through proton inelastic collisions, which can become main contributors to the synchrotron emission in several ten days since the SN. We predict that the synchrotron emission is detectable by ALMA, and suggest that it will provide a robust evidence of the existence of the “confined” CSM.
S UPPLEMENTARY M ATERIAL P ERCEPTUAL I MAGE A NOMALY D ETECTION

We present a novel method for image anomaly detection, where algorithms that use samples drawn from some distribution of “normal” data, aim to detect out-of-distribution (abnormal) samples. Our approach includes a combination of encoder and generator for mapping an image distribution to a predeﬁned latent distribution and vice versa. It leverages Generative Adversarial Networks to learn these data distributions and uses perceptual loss for the detection of image abnormality. To accomplish this goal, we introduce a new similarity metric, which expresses the perceived similarity between images and is robust to changes in image contrast. Secondly, we introduce a novel approach for the selection of weights of a multi-objective loss function (image reconstruction and distribution mapping) in the absence of a validation dataset for hyperparameter tuning. After training, our model measures the abnormality of the input image as the perceptual dissimilarity between it and the closest generated image of the modeled data distribution. The proposed approach is extensively evaluated on several publicly available image benchmarks and achieves state-of-the-art performance.
Jet Propagation in Neutron Star Mergers and GW170817

The gravitational wave event from the binary neutron star (BNS) merger GW170817 and the following multi-messenger observations present strong evidence for i) merger ejecta expanding with substantial velocities and ii) a relativistic jet which had to propagate through the merger ejecta. The ejecta’s expansion velocity is not negligible for the jet head motion, which is a fundamental difference from the other systems like collapsars and active galactic nuclei. Here we present an analytic model of the jet propagation in an expanding medium. In particular, we notice a new term in the expression of the breakout time and velocity. In parallel, we perform a series of over a hundred 2D numerical simulations of jet propagation. The BNS merger ejecta is prepared based on numerical relativity simulations of a BNS merger with the highest-resolution to date. We show that our analytic results agree with numerical simulations over a wide parameter space. Then we apply our analytic model to GW170817, and obtain two solid constraints on: i) the central engine luminosity as Liso, 0 ∼ 3 × 1049 − 2.5 × 1052 erg s−1, and on ii) the delay time between the merger and engine activation t0 − tm < 1.3 s. The engine power implies that the apparently-faint short gamma-ray burst (sGRB) sGRB 170817A is similar to typical sGRBs if observed on-axis.
Spectrum sensing based on deep learning classification for cognitive radios

Spectrum sensing is a key technology for cognitive radios. We present spectrum sensing as a classification problem and propose a sensing method based on deep learning classification. We normalize the received signal power to overcome the effects of noise power uncertainty. We train the model with as many types of signals as possible as well as noise data to enable the trained network model to adapt to untrained new signals. We also use transfer learning strategies to improve the performance for real-world signals. Extensive experiments are conducted to evaluate the performance of this method. The simulation results show that the proposed method performs better than two traditional spectrum sensing methods, i.e., maximum-minimum eigenvalue ratio-based method and frequency domain entropy-based method. In addition, the experimental results of the new untrained signal types show that our method can adapt to the detection of these new signals. Furthermore, the real-world signal detection experiment results show that the detection performance can be further improved by transfer learning. Finally, experiments under colored noise show that our proposed method has superior detection performance under colored noise, while the traditional methods have a significant performance degradation, which further validate the superiority of our method.
MRI Brain Tumor Segmentation using Random Forests and Fully Convolutional Networks

In this paper, we propose a novel learning based method for automated segmentation of brain tumor in multimodal MRI images, which incorporates two sets of machine -learned and hand crafted features. Fully convolutional networks (FCN) forms the machine learned features and texton based features are considered as hand-crafted features. Random forest (RF) is used to classify the MRI image voxels into normal brain tissues and different parts of tumors, i.e. edema, necrosis and enhancing tumor. The method was evaluated on BRATS 2017 challenge dataset. The results show that the proposed method provides promising segmentations. The mean Dice overlap measure for automatic brain tumor segmentation against ground truth is 0.86, 0.78 and 0.66 for whole tumor, core and enhancing tumor, respectively.
Grid diagrams as tools to investigate knot spaces and topoisomerase-mediated simplification of DNA topology

Grid diagrams grasp the principle of DNA topology simplification by DNA topoisomerases. Grid diagrams with their relatively simple mathematical formalism provide a convenient way to generate and model projections of various knots. It has been an open question whether these 2D diagrams can be used to model a complex 3D process such as the topoisomerase-mediated preferential unknotting of DNA molecules. We model here topoisomerase-mediated passages of double-stranded DNA segments through each other using the formalism of grid diagrams. We show that this grid diagram–based modeling approach captures the essence of the preferential unknotting mechanism, based on topoisomerase selectivity of hooked DNA juxtapositions as the sites of intersegmental passages. We show that the grid diagram–based approach provides an important, new, and computationally convenient framework for investigating entanglement in biopolymers.
The SkyMapper DR1.1 search for extremely metal-poor stars

We present and discuss the results of a search for extremely metal-poor stars based on photometry from data release DR1.1 of the SkyMapper imaging survey of the southern sky. In particular, we outline our photometric selection procedures and describe the low-resolution (R ≈ 3000) spectroscopic follow-up observations that are used to provide estimates of effective temperature, surface gravity, and metallicity ([Fe/H]) for the candidates. The selection process is very efficient: of the 2618 candidates with low-resolution spectra that have photometric metallicity estimates less than or equal to −2.0, 41 per cent have [Fe/H] ≤ −2.75 and only approximately seven per cent have [Fe/H] > −2.0 dex. The most metal-poor candidate in the sample has [Fe/H] < −4.75 and is notably carbon rich. Except at the lowest metallicities ([Fe/H] < −4), the stars observed spectroscopically are dominated by a ‘carbon-normal’ population with [C/Fe]1D, LTE ≤ +1 dex. Consideration of the A(C)1D, LTE versus [Fe/H]1D, LTE diagram suggests that the current selection process is strongly biased against stars with A(C)1D, LTE > 7.3 (predominantly CEMP-s) while any bias against stars with A(C)1D, LTE < 7.3 and [C/Fe]1D,LTE > +1 (predominantly CEMP-no) is not readily quantifiable given the uncertainty in the SkyMapper v-band DR1.1 photometry. We find that the metallicity distribution function of the observed sample has a power-law slope of Δ(Log N)/Δ[Fe/H] = 1.5 ± 0.1 dex per dex for −4.0 ≤ [Fe/H] ≤ −2.75, but appears to drop abruptly at [Fe/H] ≈ −4.2, in line with previous studies.
Mutual Aid Groups, Vulnerable Populations, and the Life Cycle

Using the life cycle as a framework, this revised edition inquires into the healing and empowering process of mutual aid groups. It sets a theoretical model for practice, and shows how social workers as group leaders can foster the process of mutual aid, in which group members help one another. The text includes many revised and updated chapters on: fundamentals of the mutual aid process; descriptions and suggestions for overcoming obstacles in mutual aid groups; Group service and its institutional benefits; and specific clinical examples of groups for children, adults, and the elderly. The new chapters on HIV-positive and AIDS patients, the homeless, and perpetrators and victims of sexual abuse and family violence, expand our current understanding of contemporary issues.
Sequence-to-sequence Pre-training with Data Augmentation for Sentence Rewriting

We study sequence-to-sequence (seq2seq) pre-training with data augmentation for sentence rewriting. Instead of training a seq2seq model with gold training data and augmented data simultaneously, we separate them to train in different phases: pre-training with the augmented data and fine-tuning with the gold data. We also introduce multiple data augmentation methods to help model pre-training for sentence rewriting. We evaluate our approach in two typical well-defined sentence rewriting tasks: Grammatical Error Correction (GEC) and Formality Style Transfer (FST). Experiments demonstrate our approach can better utilize augmented data without hurting the model's trust in gold data and further improve the model's performance with our proposed data augmentation methods. Our approach substantially advances the state-of-the-art results in well-recognized sentence rewriting benchmarks over both GEC and FST. Specifically, it pushes the CoNLL-2014 benchmark's $F_{0.5}$ score and JFLEG Test GLEU score to 62.61 and 63.54 in the restricted training setting, 66.77 and 65.22 respectively in the unrestricted setting, and advances GYAFC benchmark's BLEU to 74.24 (2.23 absolute improvement) in E&M domain and 77.97 (2.64 absolute improvement) in F&R domain.
O uso do ácido epsilon amino caproico intra-alveolar para o controle do sangramento pós-exodontia em pacientes anticoagulados

When patients on anticoagulants are submitted to dental extractions, there is risk of postoperative bleeding. The maintenance of anticoagulant therapy has been indicate donce the INR is within the therapeutic levels. Additional resources for bleeding control like the use of epsilon amino caproic acid (EACA) may be adopted. The objective of this study was to report a series of cases of tooth extractions followed by the intra-alveolar use of EACA, in patients using anticoagulants with INR ? 3.0. From 25 tooth extractions, only one patient needed professional intervention to control postoperative bleeding. The results suggest that the use of intra-alveolar EACA, associated with routine post-surgical care, may contribute to hemostasis in patients using anticoagulants.