Miracle-dz/UniFAR-FTUs huggingface dataset:
Sparse Text Generation

Current state-of-the-art text generators build on powerful language models such as GPT-2, achieving impressive performance. However, to avoid degenerate text, they require sampling from a modified softmax, via temperature parameters or ad-hoc truncation techniques, as in top-$k$ or nucleus sampling. This creates a mismatch between training and testing conditions. In this paper, we use the recently introduced entmax transformation to train and sample from a natively sparse language model, avoiding this mismatch. The result is a text generator with favorable performance in terms of fluency and consistency, fewer repetitions, and n-gram diversity closer to human text. In order to evaluate our model, we propose three new metrics for comparing sparse or truncated distributions: $\epsilon$-perplexity, sparsemax score, and Jensen-Shannon divergence. Human-evaluated experiments in story completion and dialogue generation show that entmax sampling leads to more engaging and coherent stories and conversations.
Observational Evidence for the Origin of High-energy Neutrinos in Parsec-scale Nuclei of Radio-bright Active Galaxies

Observational information on high-energy astrophysical neutrinos is being continuously collected by the IceCube observatory. However, the sources of the neutrinos are still unknown. In this study, we use radio very long baseline interferometry (VLBI) data for a complete VLBI flux density–limited sample of active galactic nuclei (AGNs). We address the problem of the origin of astrophysical neutrinos with energies above 200 TeV in a statistical manner. It is found that AGNs positionally associated with IceCube events have typically stronger parsec-scale cores than the rest of the sample. The posttrial probability of a chance coincidence is 0.2%. We select the four strongest AGNs as highly probable associations: 3C 279, NRAO 530, PKS 1741−038, and OR 103. Moreover, we find an increase of radio emission at frequencies above 10 GHz around neutrino arrival times for several other VLBI-selected AGNs on the basis of RATAN-600 monitoring. The most pronounced example of such behavior is PKS 1502+106. We conclude that AGNs with bright Doppler-boosted jets constitute an important population of neutrino sources. High-energy neutrinos are produced in their central parsec-scale regions, probably in proton–photon interactions at or around the accretion disk. Radio-bright AGNs that are likely associated with neutrinos have very diverse γ-ray properties, suggesting that γ-rays and neutrinos may be produced in different regions of AGNs and not directly related. A small viewing angle of the jet–disk axis is, however, required to detect either of them.
A Local-to-Global Approach to Multi-Modal Movie Scene Segmentation

Scene, as the crucial unit of storytelling in movies, contains complex activities of actors and their interactions in a physical environment. Identifying the composition of scenes serves as a critical step towards semantic understanding of movies. This is very challenging – compared to the videos studied in conventional vision problems, e.g. action recognition, as scenes in movies usually contain much richer temporal structures and more complex semantic information. Towards this goal, we scale up the scene segmentation task by building a large-scale video dataset MovieScenes, which contains 21K annotated scene segments from 150 movies. We further propose a local-to-global scene segmentation framework, which integrates multi-modal information across three levels, i.e. clip, segment, and movie. This framework is able to distill complex semantics from hierarchical temporal structures over a long movie, providing top-down guidance for scene segmentation. Our experiments show that the proposed network is able to segment a movie into scenes with high accuracy, consistently outperforming previous methods. We also found that pretraining on our MovieScenes can bring significant improvements to the existing approaches.
On Negative Transfer and Structure of Latent Functions in Multi-output Gaussian Processes

The multi-output Gaussian process ($\mathcal{MGP}$) is based on the assumption that outputs share commonalities, however, if this assumption does not hold negative transfer will lead to decreased performance relative to learning outputs independently or in subsets. In this article, we first define negative transfer in the context of an $\mathcal{MGP}$ and then derive necessary conditions for an $\mathcal{MGP}$ model to avoid negative transfer. Specifically, under the convolution construction, we show that avoiding negative transfer is mainly dependent on having a sufficient number of latent functions $Q$ regardless of the flexibility of the kernel or inference procedure used. However, a slight increase in $Q$ leads to a large increase in the number of parameters to be estimated. To this end, we propose two latent structures that scale to arbitrarily large datasets, can avoid negative transfer and allow any kernel or sparse approximations to be used within. These structures also allow regularization which can provide consistent and automatic selection of related outputs.
Pandemic Populism: Facebook Pages of Alternative News Media and the Corona Crisis - A Computational Content Analysis

The COVID-19 pandemic has not only had severe political, economic, and societal effects, it has also affected media and communication systems in unprecedented ways. While traditional journalistic media has tried to adapt to the rapidly evolving situation, alternative news media on the Internet have given the events their own ideological spin. Such voices have been criticized for furthering societal confusion and spreading potentially dangerous "fake news" or conspiracy theories via social media and other online channels. The current study analyzes the factual basis of such fears in an initial computational content analysis of alternative news media's output on Facebook during the early Corona crisis, based on a large German data set from January to the second half of March 2020. Using computational content analysis, methods, reach, interactions, actors, and topics of the messages were examined, as well as the use of fabricated news and conspiracy theories. The analysis revealed that the alternative news media stay true to message patterns and ideological foundations identified in prior research. While they do not spread obvious lies, they are predominantly sharing overly critical, even anti-systemic messages, opposing the view of the mainstream news media and the political establishment. With this pandemic populism, they contribute to a contradictory, menacing, and distrusting worldview, as portrayed in detail in this analysis.
Measurement of charged pion double spin asymmetries at midrapidity in longitudinally polarized 
p+p
 collisions at 
s=510  GeV

The PHENIX experiment at the Relativistic Heavy Ion Collider has measured the longitudinal double spin asymmetries, $A_{LL}$, for charged pions at midrapidity ($|\eta|<0.35$) in longitudinally polarized $p+p$ collisions at $\sqrt{s}=510$ GeV. These measurements are sensitive to the gluon spin contribution to the total spin of the proton in the parton momentum fraction $x$ range between 0.04 and 0.09. One can infer the sign of the gluon polarization from the ordering of pion asymmetries with charge alone. The asymmetries are found to be consistent with global quantum-chromodynamics fits of deep-inelastic scattering and data at $\sqrt{s}=200$ GeV, which show a nonzero positive contribution of gluon spin to the proton spin.
VoxCap: FFT-Accelerated and Tucker-Enhanced Capacitance Extraction Simulator for Voxelized Structures

VoxCap, a fast Fourier transform (FFT)-accelerated and Tucker-enhanced integral equation simulator for capacitance extraction of voxelized structures, is proposed. The VoxCap solves the surface integral equations (SIEs) for the conductor and dielectric surfaces with three key attributes that make the VoxCap highly CPU and memory-efficient for the capacitance extraction of the voxelized structures: 1) VoxCap exploits the FFTs for accelerating the matrix-vector multiplications during the iterative solution of the linear system of equations arising due to the discretization of SIEs; 2) during the iterative solution, VoxCap uses a highly effective and memory-efficient preconditioner that reduces the number of iterations significantly; and 3) VoxCap employs the Tucker decompositions to compress the block Toeplitz and circulant tensors, requiring the largest memory in the simulator. By doing so, it reduces the memory requirement of these tensors from hundreds of gigabytes to a few megabytes and the CPU time required to obtain the Toeplitz tensors from tens of minutes (even hours) to a few seconds for very large-scale problems. VoxCap is capable of accurately computing capacitance of arbitrarily shaped and large-scale voxelized structures on a desktop computer. VoxCap’s accuracy, efficiency, and capability are demonstrated through capacitance extraction of various large-scale structures, including the parallel meander lines discretized by more than a hundred million panels and analyzed on a commodity desktop computer.
TXS 2116−077: A Gamma-Ray Emitting Relativistic Jet Hosted in a Galaxy Merger

What triggers collimated relativistic outflows or jets from the centers of galaxies remains a fundamental question in astrophysics. The merging of two galaxies has been proposed to realize the conditions to successfully launch and drive such jets into the intergalactic medium. However, evidence for the operation of this mechanism is scarce. Here we report the first unambiguous detection of an ongoing merger of a narrow-line Seyfert 1 galaxy, TXS 2116−077, hosting a closely aligned, γ-ray emitting relativistic jet with a Seyfert 2 galaxy at a separation of ∼12 kpc, using the observations taken with the 8.2 m Subaru Telescope. Our subsequent follow-up observations with the 10.4 m Gran Telescopio Canarias, 4.2 m William Herschel Telescope, and Chandra X-ray observatory have provided what is likely to be the first glimpse of the merging environment hosting a closely aligned relativistic jet. Our finding that the jet is considerably younger than the merger demonstrates that jet activity can be triggered by galaxy mergers and that γ-ray detected narrow-line Seyfert 1 galaxies represent the beginning phase of that activity. These results also highlight the crucial role of mergers in shaping the fate of galaxies in their cosmological evolution and are consistent with recent studies focused on the host galaxy imaging of this enigmatic class of active galactic nuclei.
Adaptive Partial Scanning Transmission Electron Microscopy with Reinforcement Learning

Compressed sensing is applied to scanning transmission electron microscopy to decrease electron dose and scan time. However, established methods use static sampling strategies that do not adapt to samples. We have extended recurrent deterministic policy gradients to train deep LSTMs and differentiable neural computers to adaptively sample scan path segments. Recurrent agents cooperate with a convolutional generator to complete partial scans. We show that our approach outperforms established algorithms based on spiral scans, and we expect our results to be generalizable to other scan systems. Source code, pretrained models and training data is available at this https URL.
Analytical and numerical treatment of perturbed black holes in horizon-penetrating coordinates

The deviations of non-linear perturbations of black holes from the linear case are important in the context of ringdown signals with large signal-to-noise ratio. To facilitate a comparison between the two we derive several results of linear perturbation theory in coordinates which may be adopted in numerical work. Specifically, our results are derived in Kerr-Schild coordinates adjusted by a general height function. In the first part of the paper we address the questions: for an initial configuration of a massless scalar field, what is the amplitude of the excited quasinormal mode (QNM) for any observer outside outside the event horizon, and furthermore what is the resulting tail contribution? This is done by constructing the full Green's function for the problem with exact solutions of the confluent Heun equation satisfying appropriate boundary conditions. In the second part of the paper, we detail new developments to our pseudospectral numerical relativity code bamps to handle scalar fields. In the linear regime we employ precisely the Kerr-Schild coordinates treated by our previous analysis. In particular, we evolve pure QNM type initial data along with several other types of initial data and report on the presence of overtone modes in the signal.
Rethinking Spatially-Adaptive Normalization

Spatially-adaptive normalization is remarkably successful recently in conditional semantic image synthesis, which modulates the normalized activation with spatially-varying transformations learned from semantic layouts, to preserve the semantic information from being washed away. Despite its impressive performance, a more thorough understanding of the true advantages inside the box is still highly demanded, to help reduce the significant computation and parameter overheads introduced by these new structures. In this paper, from a return-on-investment point of view, we present a deep analysis of the effectiveness of SPADE and observe that its advantages actually come mainly from its semantic-awareness rather than the spatial-adaptiveness. Inspired by this point, we propose class-adaptive normalization (CLADE), a lightweight variant that is not adaptive to spatial positions or layouts. Benefited from this design, CLADE greatly reduces the computation cost while still being able to preserve the semantic information during the generation. Extensive experiments on multiple challenging datasets demonstrate that while the resulting fidelity is on par with SPADE, its overhead is much cheaper than SPADE. Take the generator for ADE20k dataset as an example, the extra parameter and computation cost introduced by CLADE are only 4.57% and 0.07% while that of SPADE are 39.21% and 234.73% respectively.
Technichal Report: Adaptive Control for Linearizable Systems Using On-Policy Reinforcement Learning

This paper proposes a framework for adaptively learning a feedback linearization-based tracking controller for an unknown system using discrete-time model-free policy-gradient parameter update rules. The primary advantage of the scheme over standard model-reference adaptive control techniques is that it does not require the learned inverse model to be invertible at all instances of time. This enables the use of general function approximators to approximate the linearizing controller for the system without having to worry about singularities. However, the discrete-time and stochastic nature of these algorithms precludes the direct application of standard machinery from the adaptive control literature to provide deterministic stability proofs for the system. Nevertheless, we leverage these techniques alongside tools from the stochastic approximation literature to demonstrate that with high probability the tracking and parameter errors concentrate near zero when a certain persistence of excitation condition is satisﬁed. A simulated example of a double pendulum demonstrates the utility of the proposed theory. 1
Intimate Partner Violence During Pregnancy and Prenatal Care Attendance in Abuja, Nigeria.

Intimate Partner Violence During Pregnancy and Prenatal Care Attendance in Abuja, Nigeria by Dorothy Ijeoma Ezekwe-Anya MPH, Walden University, 2011 BSc Health System Management, University of Baltimore, 2009 Dissertation Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy Public Health
Awareness About the Role of Bariatric Surgery in Mortality and Morbidity among General Population of Albaha City

Background: Obesity is an emerging health concern worldwide and in Saudi Arabia. Until now bariatric surgery is the most effective treatment for morbid obesity. Objective: The aim of this study was to measure awareness about the role of bariatric surgery in mortality and morbidity among general population in Albaha city in Saudi Arabia. Methods: A questionnaire was distributed among the general public in Albaha city, Saudi Arabia. The questionnaire consisted of two parts, the first one included personal data and the second one was concerned with awareness and knowledge of people regarding effectiveness and complications of bariatric surgery. Only completed questionnaires without missing data were subjected to statistical analysis. Results: The majority of respondents opposed surgery and didn't believe in its effectiveness compared with other treatment lines. There were false beliefs about the regaining of weight after surgery and exaggeration of its complications. The most common causes for refusal of surgery were lack of need and fear of complications while the most common motives for acceptance were desire of more weight loss and health benefits. Conclusion: Health education is necessary to increase patients' awareness and knowledge about bariatric surgery. The topics to be focused upon are the expected benefits after surgery, the maintenance of weight and the nature and incidence of surgical complications.
Data-Driven Storytelling

This report documents the program and the outcomes of Dagstuhl Seminar 16061 “Data-Driven Storytelling”. Close to forty researchers and practitioners descended on Schloss Dagstuhl to forge an interdisciplinary agenda on the topic of data-driven storytelling using visualization in early February, 2016. With burgeoning research interest in understanding what makes visualization effective for communication, and with practitioners pushing the envelope of the craft of visual communication, the meeting put different modes of thinking between computer science researchers and data visualization practitioners in close proximity for a week.
FAKTOR-FAKTOR YANG MEMPENGARUHI ANEMIA PADA IBU HAMIL DI PUSKESMAS PADEDIWATU KABUPATEN SUMBA BARAT

ABSTRACT Pregnancy is the beginning of a new life period of growth. The period of pregnancy is a very important period for the formation of quality human resources in the future, because the growth of children will be determined by the conditions at the time the fetus in the womb. Anemia is a health problem especially for women of reproductive age and pregnant women. The most common causes of anemia in pregnancy are iron deficiency, folic acid, and acute bleeding and can occur due to interactions between the two.The purpose of this study was to find out the description of factors That affecting anemia in pregnant women at Padediwatu Sumba Barat Public Health Center.The method used in this research is descriptive with cross sectional study design. Total respondent 63 with sampling technique using total sampling with instrument used in this research is questionnaire.Respondents with age category 20-30 years with high school education level, diet always, adhering to consume tablet Fe, socioeconomic status of minimum and maximum wage, with distance of pregnancy> 2 years and most respondents had mild anemia.For respondents who have anemia still maintain the condition so it does not arrive at severe anemia. Keywords: Anemia, Pregnant Woman
Quality of life among hepatitis C patients on treatment, Minia, Egypt

Chronic hepatitis C (CHC) is an infectious disease caused by the hepatitis C virus (HCV). According to World Health Organization (WHO), around 250 million people globally are infected with HCV, and an estimated 71 million people have chronic hepatitis C infection, and around 170 million people are chronic carriers of the virus, which can lead to cirrhosis, liver failure, and hepatocellular carcinoma. Egypt has the largest burden of HCV infection in the world, with a 10% prevalence of chronic HCV infection among persons aged 15–59 years. HCV transmission is ongoing in Egypt and incidence rates estimated between 2 and 6 per 1000 every year, (170,000 new infections annually). 4
The Meaning of Involvement in an Exercise and Health Promotion Program for People with Diabetes

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Occupational health and safety indicators of Turkey and their relationships with social and economic development factors between 1998 and 2014

ABSTRACT The aim of this descriptive study is to analyse occupational health and safety status of Turkey between 1998 and 2014 and examine its relationships with some socioeconomic factors. Data reported by Social Security Institution of Turkey and two types of incidence rates of occupational accidents determined by the institution were used in analysis; the first uses working hours, while the latter uses the number of employees as basis. A third type of incidence rate involving occupational deaths per 100,000 employees was used in comparing Turkey and EU, and it was found significantly higher from developed countries like EU-15 (t = −10.032, P < 0.001). Incidence of occupational accidents and indicators, such as human development index (HDI), GDP, literacy rate, and budget spending for education in Turkey between 1998 and 2014, were correlated with each other, and a significant negative relationship was found between fatal occupational accidents and literacy rate (r = −0.577, P < 0.05). Furthermore, a negative significant relationship between growth rate (GDP) and HDI (r = −0.813, P < 0.01) was observed. A positive relationship between GDP and incidence of fatal accidents was found, but it was insignificant (r = 0.502, P > 0.05). Economically growing populations can protect their workers against occupational hazards by taking effective and preventive measures for occupational health and safety.