Miracle-dz/UniFAR-FTUs huggingface dataset:
Mining Discourse Markers for Unsupervised Sentence Representation Learning

Current state of the art systems in NLP heavily rely on manually annotated datasets, which are expensive to construct. Very little work adequately exploits unannotated data – such as discourse markers between sentences – mainly because of data sparseness and ineffective extraction methods. In the present work, we propose a method to automatically discover sentence pairs with relevant discourse markers, and apply it to massive amounts of data. Our resulting dataset contains 174 discourse markers with at least 10k examples each, even for rare markers such as “coincidentally” or “amazingly”. We use the resulting data as supervision for learning transferable sentence embeddings. In addition, we show that even though sentence representation learning through prediction of discourse marker yields state of the art results across different transfer tasks, it’s not clear that our models made use of the semantic relation between sentences, thus leaving room for further improvements.
Depth From a Polarisation + RGB Stereo Pair

In this paper, we propose a hybrid depth imaging system in which a polarisation camera is augmented by a second image from a standard digital camera. For this modest increase in equipment complexity over conventional shape-from-polarisation, we obtain a number of benefits that enable us to overcome longstanding problems with the polarisation shape cue. The stereo cue provides a depth map which, although coarse, is metrically accurate. This is used as a guide surface for disambiguation of the polarisation surface normal estimates using a higher order graphical model. In turn, these are used to estimate diffuse albedo. By extending a previous shape-from-polarisation method to the perspective case, we show how to compute dense, detailed maps of absolute depth, while retaining a linear formulation. We show that our hybrid method is able to recover dense 3D geometry that is superior to state-of-the-art shape-from-polarisation or two view stereo alone.
Describing Like Humans: On Diversity in Image Captioning

Recently, the state-of-the-art models for image captioning have overtaken human performance based on the most popular metrics, such as BLEU, METEOR, ROUGE and CIDEr. Does this mean we have solved the task of image captioning The above metrics only measure the similarity of the generated caption to the human annotations, which reflects its accuracy. However, an image contains many concepts and multiple levels of detail, and thus there is a variety of captions that express different concepts and details that might be interesting for different humans. Therefore only evaluating accuracy is not sufficient for measuring the performance of captioning models --- the diversity of the generated captions should also be considered. In this paper, we proposed a new metric for measuring the diversity of image captions, which is derived from latent semantic analysis and kernelized to use CIDEr similarity. We conduct extensive experiments to re-evaluate recent captioning models in the context of both diversity and accuracy. We find that there is still a large gap between the model and human performance in terms of both accuracy and diversity, and the models that have optimized accuracy (CIDEr) have low diversity. We also show that balancing the cross-entropy loss and CIDEr reward in reinforcement learning during training can effectively control the tradeoff between diversity and accuracy of the generated captions.
Bridging Adversarial Robustness and Gradient Interpretability

Adversarial training is a training scheme designed to counter adversarial attacks by augmenting the training dataset with adversarial examples. Surprisingly, several studies have observed that loss gradients from adversarially trained DNNs are visually more interpretable than those from standard DNNs. Although this phenomenon is interesting, there are only few works that have offered an explanation. In this paper, we attempted to bridge this gap between adversarial robustness and gradient interpretability. To this end, we identified that loss gradients from adversarially trained DNNs align better with human perception because adversarial training restricts gradients closer to the image manifold. We then demonstrated that adversarial training causes loss gradients to be quantitatively meaningful. Finally, we showed that under the adversarial training framework, there exists an empirical trade-off between test accuracy and loss gradient interpretability and proposed two potential approaches to resolving this trade-off.
Highly cited references in PLOS ONE and their in-text usage over time

In this article, we describe highly cited publications in a PLOS ONE full-text corpus. For these publications, we analyse the citation contexts concerning their position in the text and their age at the time of citing. By selecting the perspective of highly cited papers, we can distinguish them based on the context during citation even if we do not have any other information source or metrics. We describe the top cited references based on how, when and in which context they are cited. The focus of this study is on a time perspective to explain the nature of the reception of highly cited papers. We have found that these references are distinguishable by the IMRaD sections of their citation. And further, we can show that the section usage of highly cited papers is time-dependent: the longer the citation interval, the higher the probability that a reference is cited in a method section.
How to pick the domain randomization parameters for sim-to-real transfer of reinforcement learning policies?

Recently, reinforcement learning (RL) algorithms have demonstrated remarkable success in learning complicated behaviors from minimally processed input. However, most of this success is limited to simulation. While there are promising successes in applying RL algorithms directly on real systems, their performance on more complex systems remains bottle-necked by the relative data inefficiency of RL algorithms. Domain randomization is a promising direction of research that has demonstrated impressive results using RL algorithms to control real robots. At a high level, domain randomization works by training a policy on a distribution of environmental conditions in simulation. If the environments are diverse enough, then the policy trained on this distribution will plausibly generalize to the real world. A human-specified design choice in domain randomization is the form and parameters of the distribution of simulated environments. It is unclear how to the best pick the form and parameters of this distribution and prior work uses hand-tuned distributions. This extended abstract demonstrates that the choice of the distribution plays a major role in the performance of the trained policies in the real world and that the parameter of this distribution can be optimized to maximize the performance of the trained policies in the real world
What students know about climate change? a case study of high school students in Samboja, Indonesia

Indonesia is among the top 10 highest countries in the world producing huge amount of greenhouse gases (GHG) to the atmosphere. However, climate change including its impacts might not be understandable by public in Indonesia. This is because climate change is not a crucial issue in this developing country. Education can play important roles in supporting national policies in reducing GHG in Indonesia by communicating climate change and in raising environmental awareness among people including students in schools. This study aims to determine high school students’ perceptions about climate change, to provide insights for curriculum planning to combat climate change. Furthermore, this study can also provide some strategies to the government changes by improving formal education in combating climate change. This study is conducted in the 6 selected high schools in Samboja district, Kutai Kartanegara regency, East Kalimantan province, Indonesia. Data collection was conducted from February to March 2017. Primary data is collected by using questionnaires, which are distributed to the 1st, 2nd and 3rd grade students in each selected high schools. In total there are 360 students participating in this study. Results shows that majority of students in this study have heard about climate change, however their answer are inconsistent. This indicates that these students do not fully understand about climate change. Furthermore, students also do not know about the main causes of climate change. In conclusion, climate change issues need to be elaborated in science curriculum. It is suggested that the government should introduce policies in education sectors to inform about climate change.
Fast video object segmentation with Spatio-Temporal GANs

Learning descriptive spatio-temporal object models from data is paramount for the task of semi-supervised video object segmentation. Most existing approaches mainly rely on models that estimate the segmentation mask based on a reference mask at the first frame (aided sometimes by optical flow or the previous mask). These models, however, are prone to fail under rapid appearance changes or occlusions due to their limitations in modelling the temporal component. On the other hand, very recently, other approaches learned long-term features using a convolutional LSTM to leverage the information from all previous video frames. Even though these models achieve better temporal representations, they still have to be fine-tuned for every new video sequence. In this paper, we present an intermediate solution and devise a novel GAN architecture, FaSTGAN, to learn spatio-temporal object models over finite temporal windows. To achieve this, we concentrate all the heavy computational load to the training phase with two critics that enforce spatial and temporal mask consistency over the last K frames. Then at test time, we only use a relatively light regressor, which reduces the inference time considerably. As a result, our approach combines a high resiliency to sudden geometric and photometric object changes with efficiency at test time (no need for fine-tuning nor post-processing). We demonstrate that the accuracy of our method is on par with state-of-the-art techniques on the challenging YouTube-VOS and DAVIS datasets, while running at 32 fps, about 4x faster than the closest competitor.
Genetic drift in range expansions is very sensitive to density feedback in dispersal and growth

Theory predicts rapid genetic drift in expanding populations due to the serial founder effect at the expansion front. Yet, many natural populations maintain high genetic diversity in the newly colonized regions. Here, we investigate whether density-dependent dispersal could provide a resolution of this paradox. We find that genetic drift is dramatically suppressed when dispersal rates increase with the population density because many more migrants from the diverse, high-density regions arrive at the expansion edge. When density-dependence is weak or negative, the effective population size of the front scales only logarithmically with the carrying capacity. The dependence, however, switches to a sublinear power law and then to a linear increase as the density-dependence becomes strongly positive. To understand these results, we introduce a unified framework that predicts how the strength of genetic drift depends on the density-dependence in both dispersal and growth. This theory reveals that the transitions between different regimes of diversity loss are controlled by a single, universal parameter: the ratio of the expansion velocity to the geometric mean of dispersal and growth rates at expansion edge. Importantly, our results suggest that positive density-dependence could dramatically alter evolution in expanding populations even when its contributions to the expansion velocity is small.
Gravitational waves from bodies orbiting the Galactic center black hole and their detectability by LISA

Aims. We present the first fully relativistic study of gravitational radiation from bodies in circular equatorial orbits around the massive black hole at the Galactic center, Sgr A* and we assess the detectability of various kinds of objects by the gravitational wave detector LISA. Methods. Our computations are based on the theory of perturbations of the Kerr spacetime and take into account the Roche limit induced by tidal forces in the Kerr metric. The signal-to-noise ratio in the LISA detector, as well as the time spent in LISA band, are evaluated. We have implemented all the computational tools in an open-source SageMath package, within the Black Hole Perturbation Toolkit framework. Results. We find that white dwarfs, neutrons stars, stellar black holes, primordial black holes of mass larger than 10−4 M⊙, main-sequence stars of mass lower than ∼2.5 M⊙, and brown dwarfs orbiting Sgr A* are all detectable in one year of LISA data with a signal-to-noise ratio above 10 for at least 105 years in the slow inspiral towards either the innermost stable circular orbit (compact objects) or the Roche limit (main-sequence stars and brown dwarfs). The longest times in-band, of the order of 106 years, are achieved for primordial black holes of mass ∼10−3 M⊙ down to 10−5 M⊙, depending on the spin of Sgr A*, as well as for brown dwarfs, just followed by white dwarfs and low mass main-sequence stars. The long time in-band of these objects makes Sgr A* a valuable target for LISA. We also consider bodies on close circular orbits around the massive black hole in the nucleus of the nearby galaxy M 32 and find that, among them, compact objects and brown dwarfs stay for 103–104 years in LISA band with a one-year signal-to-noise ratio above ten.
Deciphering the underlying mechanisms of the pharyngeal motions in Caenorhabditis elegans.

The pharynx of the nematode Caenorhabditis elegans is a neuromuscular pump that exhibits two typical motions: pumping and peristalsis. While the dynamics of these motions are well characterized, the underlying mechanisms generating most of them are not known. In this paper, we propose comprehensive and detailed mechanisms that can explain the various observed dynamics of the different pharyngeal areas: the dynamics of the pumping muscles - corpus, anterior isthmus, and terminal bulb - and the peristalsis dynamics of the posterior isthmus muscles. While the suggested mechanisms are consistent with all available relevant data, the assumptions on which they are based and the open questions they raise could point at additional interesting research directions on the C. elegans pharynx. We are hoping that appropriate experiments on the nematode will eventually corroborate our results, and improve our understanding of the functioning of the C. elegans pharynx, and possibly of the mammalian digestive system.
Bounds on Wahl singularities from symplectic topology

Let X be a minimal surface of general type with positive geometric genus ($b_+ > 1$) and let $K^2$ be the square of its canonical class. Building on work of Khodorovskiy and Rana, we prove that if X develops a Wahl singularity of length $\ell$ in a Q-Gorenstein degeneration, then $\ell \leq 4K^2 + 7$. This improves on the current best-known upper bound due to Lee ($\ell \leq 400(K^2)^4$). Our bound follows from a stronger theorem constraining symplectic embeddings of certain rational homology balls in surfaces of general type. In particular, we show that if the rational homology ball $B_{p,1}$ embeds symplectically in a quintic surface, then $p \leq 12$, partially answering the symplectic version of a question of Kronheimer.
Prediction of premature all-cause mortality: A prospective general population cohort study comparing machine-learning and standard epidemiological approaches

Background Prognostic modelling using standard methods is well-established, particularly for predicting risk of single diseases. Machine-learning may offer potential to explore outcomes of even greater complexity, such as premature death. This study aimed to develop novel prediction algorithms using machine-learning, in addition to standard survival modelling, to predict premature all-cause mortality. Methods A prospective population cohort of 502,628 participants aged 40–69 years were recruited to the UK Biobank from 2006–2010 and followed-up until 2016. Participants were assessed on a range of demographic, biometric, clinical and lifestyle factors. Mortality data by ICD-10 were obtained from linkage to Office of National Statistics. Models were developed using deep learning, random forest and Cox regression. Calibration was assessed by comparing observed to predicted risks; and discrimination by area under the ‘receiver operating curve’ (AUC). Findings 14,418 deaths (2.9%) occurred over a total follow-up time of 3,508,454 person-years. A simple age and gender Cox model was the least predictive (AUC 0.689, 95% CI 0.681–0.699). A multivariate Cox regression model significantly improved discrimination by 6.2% (AUC 0.751, 95% CI 0.748–0.767). The application of machine-learning algorithms further improved discrimination by 3.2% using random forest (AUC 0.783, 95% CI 0.776–0.791) and 3.9% using deep learning (AUC 0.790, 95% CI 0.783–0.797). These ML algorithms improved discrimination by 9.4% and 10.1% respectively from a simple age and gender Cox regression model. Random forest and deep learning achieved similar levels of discrimination with no significant difference. Machine-learning algorithms were well-calibrated, while Cox regression models consistently over-predicted risk. Conclusions Machine-learning significantly improved accuracy of prediction of premature all-cause mortality in this middle-aged population, compared to standard methods. This study illustrates the value of machine-learning for risk prediction within a traditional epidemiological study design, and how this approach might be reported to assist scientific verification.
HIV index testing to improve HIV positivity rate and linkage to care and treatment of sexual partners, adolescents and children of PLHIV in Lesotho

Despite years of HIV testing and other interventions, Lesotho continues to experience an incredibly high HIV burden. Prevalence of HIV among children ages 0–14 years is at 2.1% and 25.6% among adults ages 15–59 years. Among adults living with HIV, 77.2% know their status, 90.2% of those with known HIV positive status are currently receiving ART and 88.3% are virally suppressed. In order to identify adults, adolescents and children at high risk of HIV infection, Population Services International (PSI)/Lesotho with support from the Centers for Disease Control and Prevention (CDC) introduced the HIV Index testing model in 2015. PLHIV recruited for index testing, were accessed through health facilities and community testing at PSI New Start channels in five districts. Consenting index clients received home visits for HIV testing of their biological children and sexual partners with unknown status. Routine monitoring of data gathered between May 2015 and November 2017 was analyzed to assess feasibility of this approach. For HIV index testing, 49.2% of children below 15 years and 37.3% of adolescents ages 15–19 were first time testers while 18.8% of all adults aged 20 years and above tested were testing for the first time. Higher HIV positivity rates among clients tested through the HIV index testing model across all age groups in comparison to other HIV testing models were statistically significant. Among children ages 2–14 years, the HIV positivity rate was 1.4%, adolescents ages 15–19 years had a positivity rate of 2.4% and adults ages 20 years and above had a positivity rate of 17.6%. Linkage rates of 92%, 73% and 72% for children, adolescents and adults, respectively, achieved with the HIV index testing model were higher than linkage rates observed with other HIV testing models. Results indicate that testing of biological children and sexual partners utilizing the HIV index testing model can be viable to identify and link children, adolescents and adults into care and treatment.
Models and Simulations for the Photometric LSST Astronomical Time Series Classification Challenge (PLAsTiCC)

We describe the simulated data sample for the Photometric Large Synoptic Survey Telescope (LSST) Astronomical Time Series Classification Challenge (PLAsTiCC), a publicly available challenge to classify transient and variable events that will be observed by the LSST, a new facility expected to start in the early 2020s. The challenge was hosted by Kaggle, ran from 2018 September 28 to December 17, and included 1094 teams competing for prizes. Here we provide details of the 18 transient and variable source models, which were not revealed until after the challenge, and release the model libraries at https://doi.org/10.5281/zenodo.2612896. We describe the LSST Operations Simulator used to predict realistic observing conditions, and we describe the publicly available SNANA simulation code used to transform the models into observed fluxes and uncertainties in the LSST passbands (ugrizy). Although PLAsTiCC has finished, the publicly available models and simulation tools are being used within the astronomy community to further improve classification, and to study contamination in photometrically identified samples of SN Ia used to measure properties of dark energy. Our simulation framework will continue serving as a platform to improve the PLAsTiCC models, and to develop new models.
Caring for Infectious Disease in the Prehospital Setting: A Qualitative Analysis of EMS Providers Experiences and Suggestions for Improvement

Abstract Objective: The purpose of this study was to examine the attitudes and behaviors of emergency medical technicians (EMTs) and Paramedics when faced with the decision to care for patients with suspected Ebola Virus Disease (EVD) and to illicit suggestions for improvement of infectious disease (ID) preparedness. Methods: A convenience sample of 22 EMT/Paramedics were recruited from an emergency department at one of the designated Ebola centers. Each provider participated in one of three on-site focus groups. Participants answered questions about how they gained their knowledge, felt about caring for EVD patients, made decisions about caring for EVD patients, and suggestions for improvement of ID preparedness. Focus groups were recorded, transcribed, and coded using inductive content analysis. Results: Analysis revealed five prominent themes: reactions to scare, education/training, danger, decision making, and suggestions for future responses. Overall, first responders were excited to be a part of the response to EVD. They were more comfortable caring for EVD patients if they received adequate education and transparency from the administration. This resulted in a decreased perceived danger of the disease and decreased hesitancy when caring for EVD patients. However, those that expressed the most hesitancy also expressed the most emotional distress. Suggestions for improvement of ID preparedness included continuing education, tiered training models, peer training models, collaboration between emergency medical services (EMS) systems, better communication between departments, and the development of an infectious disease response team. Conclusions: Although first responders were excited to be a part of the response to EVD, this did not come without hesitation and emotional distress. Some of these concerns may be mitigated by first providing a clear definition of “duty to care,” followed by interventions such as the development of clear and consistent ID preparedness training and interventions that address the emotional distress experienced by these providers.