Miracle-dz/UniFAR-FTUs huggingface dataset:
GDPR COMPLIANCE IN NORWEGIAN COMPANIES

The General Data Protection Regulation (GDPR) became effective from May 25th, 2018 in the EU and influences any company that collects and stores personal data about European citizens. Our research aim is to explore opportunities and challenges that Norwegian companies face when complying with GDPR. First, we studied the 99 articles that constitutes GDPR. Second, we conducted a survey questionnaire and third, we took part in the GDPR project of one large company during spring 2018. Our contribution consists of insights and descriptions of opportunities and challenges that Norwegian companies face when complying with GDPR. Our main findings include that the majority of our respondents was well informed about the new regulation and they rated themselves as well prepared. They even saw some positive aspects, like gaining more control over the company’s data and business procedures. The greatest concern is how to comply with Article 17: Right to erasure (“right to be forgotten”). In addition, this paper contributes by identifying eleven of 99 GDPR articles that primarily influence a company’s IT-systems. Our study should be of interest to company managers and it will remain relevant in the time after the GDPR implementation date. In this regard, one of our respondents eloquently stated: “Complying with GDPR is not a goal to be reached, it is the start of a journey”.
Effect of Statutory and Pictorial Health Warning among People Consuming Tobacco Products in Pune City, India – A Questionnaire Study

Context: Government is trying diligently to reduce the sale of tobacco products among the consumers by taking proactive measures like mandatory statutory and pictorial on the tobacco packaging. This study was conducted to check the effectiveness of such warnings amongst consumers. Aim: To assess the effect of statutory and pictorial health warning among individuals consuming smokeless tobacco product. Settings and design: The questionnaire study was conducted among 203 smokeless tobacco consumers of Pune from 01st August to 30th August 2015. Material and Methods: 203 consumers formed the sample size of the study. 12 closed ended questions were asked by interview method. The responses were collected, tabulated and analysed. Statistical Analyses used: Descriptive analysis. Results: Mean age of the study participants was 37.67±8.14 ranging from 23-65. 198(97.5) of the study participants were aware of the pictorial warning. Though 157(77.3) wanted to quit tobacco, 33(18.2) were not able to understand pictorial warning. Conclusion: It was concluded that though there was awareness about pictorial and statutory warnings which are found to be effective tools to help consumers quit tobacco. Poor quality of packaging and absence of warnings on few of smokeless tobacco did not serve the purpose. Key words: Health warnings, Pictorial Warning, Statutory Warning, Smokeless tobacco, Tobacco cessation
CAFENet: Class-Agnostic Few-Shot Edge Detection Network

We tackle a novel few-shot learning challenge, which we call few-shot semantic edge detection, aiming to localize crisp boundaries of novel categories using only a few labeled samples. We also present a Class-Agnostic Few-shot Edge detection Network (CAFENet) based on meta-learning strategy. CAFENet employs a semantic segmentation module in small-scale to compensate for lack of semantic information in edge labels. The predicted segmentation mask is used to generate an attention map to highlight the target object region, and make the decoder module concentrate on that region. We also propose a new regularization method based on multi-split matching. In meta-training, the metric-learning problem with high-dimensional vectors are divided into small subproblems with low-dimensional sub-vectors. Since there is no existing dataset for few-shot semantic edge detection, we construct two new datasets, FSE-1000 and SBD-$5^i$, and evaluate the performance of the proposed CAFENet on them. Extensive simulation results confirm the performance merits of the techniques adopted in CAFENet.
Rotate-and-Render: Unsupervised Photorealistic Face Rotation From Single-View Images

Though face rotation has achieved rapid progress in recent years, the lack of high-quality paired training data remains a great hurdle for existing methods. The current generative models heavily rely on datasets with multi-view images of the same person. Thus, their generated results are restricted by the scale and domain of the data source. To overcome these challenges, we propose a novel unsupervised framework that can synthesize photo-realistic rotated faces using only single-view image collections in the wild. Our key insight is that rotating faces in the 3D space back and forth, and re-rendering them to the 2D plane can serve as a strong self-supervision. We leverage the recent advances in 3D face modeling and high-resolution GAN to constitute our building blocks. Since the 3D rotation-and-render on faces can be applied to arbitrary angles without losing details, our approach is extremely suitable for in-the-wild scenarios (i.e. no paired data are available), where existing methods fall short. Extensive experiments demonstrate that our approach has superior synthesis quality as well as identity preservation over the state-of-the-art methods, across a wide range of poses and domains. Furthermore, we validate that our rotate-and-render framework naturally can act as an effective data augmentation engine for boosting modern face recognition systems even on strong baseline models
Technological Diffusion Across Hospitals: The Case of a Revenue-Generating Practice

Productivity-raising technologies tend to diuse slowly, particularly in the health care sector. To understand how incentives drive adoption, I study a technology that generates revenue for hospitals: the practice of submitting detailed documentation about patients. After a 2008 reform, hospitals were able to raise their total Medicare revenue over 2% by always specifying a patient’s type of heart failure. I find that hospitals only captured around half of this revenue, indicating that large frictions impeded takeup. The key barrier is a principal-agent problem, since doctors supply the valuable information but are not paid for it. Exploiting the fact that many doctors practice at multiple hospitals, I find that four-fifths of the dispersion in adoption reflects dierences in the ability of hospitals to extract documentation from physicians. Hospital adoption is also robustly correlated with the ability to generate survival for heart attack patients and the use of inexpensive survival-raising standards of care, suggesting that principal-agent problems drive disparities in quality more generally. These findings highlight the importance of agency conflicts in explaining variations in health care performance.
Transformer Networks for Trajectory Forecasting

Most recent successes on forecasting the people motion are based on LSTM models and all most recent progress has been achieved by modelling the social interaction among people and the people interaction with the scene. We question the use of the LSTM models and propose the novel use of Transformer Networks for trajectory forecasting. This is a fundamental switch from the sequential step-by-step processing of LSTMs to the only-attention-based memory mechanisms of Transformers. In particular, we consider both the original Transformer Network (TF) and the larger Bidirectional Transformer (BERT), state-of-the-art on all natural language processing tasks. Our proposed Transformers predict the trajectories of the individual people in the scene. These are “simple” models because each person is modelled separately without any complex human-human nor scene interaction terms. In particular, the TF model without bells and whistles yields the best score on the largest and most challenging trajectory forecasting benchmark of TrajNet [1]. Additionally, its extension which predicts multiple plausible future trajectories performs on par with more engineered techniques on the 5 datasets of ETH [2]+UCY [3]. Finally, we show that Transformers may deal with missing observations, as it may be the case with real sensor data. Code is available at github.com/FGiuliari/Trajectory-Transformer.
Globular clusters in the Sagittarius stream

We reconsider the case for the association of Galactic globular clusters to the tidal stream of the Sagittarius dwarf spheroidal galaxy (Sgr dSph) using Gaia DR2 data. We used RR Lyrae variables to trace the stream in 6D and we selected clusters matching the observed stream in position and velocity. In addition to the clusters residing in the main body of the galaxy (M 54, Ter 8, Ter 7, Arp 2) we confirm the membership of Pal 12 and Whiting 1 to the portion of the trailing arm populated by stars lost during recent perigalactic passages. NGC 2419, NGC 5634, and NGC 4147 are very interesting candidates, possibly associated with more ancient wraps of the Sagittarius stream. With the exception of M 54, which lies within the stellar nucleus of the galaxy, we note that all these clusters are found in the trailing arm of the stream. The selected clusters are fully consistent with the [Fe/H] versus [Mg/Fe], [Ca/Fe] patterns and the age-metallicity relation displayed by field stars in the main body of Sgr dSph.
Floquet Topological Phases of Non-Hermitian Disordered Systems

The non-Hermiticity caused breakdown of bulk-boundary correspondence in topological phase transition was curled by considering the skin effect for the chirally symmetric systems. However, as a powerful tool in engineering exotic topological phases, periodic driving cannot preserve the chiral symmetry of the systems. Here, we investigate the topological phases of periodically driven one-dimensional non-Hermitian disordered system. Using Floquet theorem, we propose a scheme to retrieve the bulk-boundary correspondence and establish a general description to the topological phase transition for such nonequilibrium system. The exemplification of our method in the non-Hermitian Su-Schrieffer-Heeger model shows that diverse exotic topological phases absent in the static case are induced by the periodic driving. Further study reveals that the extra non-Hermitian Floquet topological Anderson insulator phases are induced by the disorder. Our result supplies a useful way to artificially synthesize exotic phases by periodic driving in the non-Hermitian system.
Predictors of successful induction of labour at a tertiary obstetric service in Southwest Nigeria

Context: Induction of labour is a useful obstetric intervention, yet it is underutilized in Africa. Recommendations for practice may reduce its unmet need. Objective: This study aims to determine labour induction success rates and identify predictors of outcome. Study Design, Setting and Patients: This was a retrospective, descriptive, cross-sectional study of 104 women who had induction of labour at the University College Hospital, Nigeria. Main Outcome Measures: Primary outcomes were vaginal delivery within 24 hours and caesarean delivery. Analyses were done by Chi-square tests, t-tests and logistic regression. Results: Labour induction rate was 12.7%; most were performed on account of post-dated pregnancies and pre-labour rupture of membranes. Forty-six, (44.2%) had vaginal delivery within 24 hours whereas induction failed (i.e. caesarean delivery) in 38 (36.5%). The mean duration of the process was 12.0 ± 6.6 hours with misoprostol, 8 hours less than with oxytocin (P < 0.01). Misoprostol was significantly more likely to result in delivery within 24 hours in comparison to extra-amniotic transcervical catheter for ripening (P = 0.02, OR = 5.1, 95% CI = 1.2-21.1), and to oxytocin for induction (P = 0.03, OR = 6.5, 95% CI = 1.2-36.3), respectively. Adverse effects were infrequent and comparable with either method. Conclusion: Success rate needs to be improved upon. Higher parity, later gestation and misoprostol ripening or induction are associated with successful outcomes. It is recommended that clients′ experience may be improved by commencing misoprostol cervical ripening the night before induction and administering the medication orally rather than vaginally.
Effect of electric current on the optical orientation of interface electrons in AlGaAs/GaAs heterostructures

The effect of a lateral electric current on the photoluminescence H band of an AlGaAs/GaAs heterostructure is investigated. The photoluminescence intensity and optical orientation of electrons contributing to the H band are studied by means of continuous-wave and time-resolved photoluminescence spectroscopy and time-resolved Kerr rotation. It is shown that the H band is due to recombination of the heavy holes localized at the heterointerface with photoexcited electrons attracted to the heterointerface from the GaAs layer. Two lines with significantly different decay times constitute the H band: a short-lived high-energy one and a long-lived low-energy one. The high-energy line originates from recombination of electrons freely moving along the structure plane, while the low-energy one is due to recombination of donor-bound electrons near the interface. Application of a lateral electric field of \ensuremath{\sim}100--200 V/cm results in a quenching of both lines. This quenching is due to a decrease of electron concentration near the heterointerface as a result of a photocurrent-induced heating of electrons in the GaAs layer. On the contrary, electrons near the heterointerface are effectively cooled, so the donors near the interface are not completely empty up to \ensuremath{\sim}100 V/cm, which is in stark contrast with the case of bulk materials. The optical spin polarization of the donor-bound electrons near the heterointerface weakly depends on the electric field. Their polarization kinetics is determined by the spin dephasing in the hyperfine fields of the lattice nuclei. The long spin memory time (g40 ns) can be associated with suppression of the Bir-Aronov-Pikus mechanism of spin relaxation for electrons.
Network disruption: maximizing disagreement and polarization in social networks

Recent years have seen a marked increase in the spread of misinformation, a phenomenon which has been accelerated and amplified by social media such as Facebook and Twitter. While some actors spread misinformation to push a specific agenda, it has also been widely documented that others aim to simply disrupt the network by increasing disagreement and polarization across the network and thereby destabilizing society. Popular social networks are also vulnerable to large-scale attacks. Motivated by this reality, we introduce a simple model of network disruption where an adversary can take over a limited number of user profiles in a social network with the aim of maximizing disagreement and/or polarization in the network. We investigate this model both theoretically and empirically. We show that the adversary will always change the opinion of a taken-over profile to an extreme in order to maximize disruption. We also prove that an adversary can increase disagreement / polarization at most linearly in the number of user profiles it takes over. Furthermore, we present a detailed empirical study of several natural algorithms for the adversary on both synthetic networks and real world (Reddit and Twitter) data sets. These show that even simple, unsophisticated heuristics, such as targeting centrists, can disrupt a network effectively, causing a large increase in disagreement / polarization. Studying the problem of network disruption through the lens of an adversary thus highlights the seriousness of the problem.
Enhancement of the tidal disruption event rate in galaxies with a nuclear star cluster: from dwarfs to ellipticals

We compute the tidal disruption event (TDE) rate around local massive black holes (MBHs) with masses as low as $2.5\times10^4 {\rm M}_\odot$, thus probing the dwarf regime for the first time. We select a sample of 37 galaxies for which we have the surface stellar density profile, a dynamical estimate of the mass of the MBH, and 6 of which, including our Milky Way, have a resolved nuclear star cluster (NSC). For the Milky Way, we find a total TDE rate of $\sim 10^{-4}{\rm yr}^{-1}$ when taking the NSC in account, and $\sim 10^{-7} {\rm yr}^{-1}$ otherwise. TDEs are mainly sourced from the NSC for light ($<3\times 10^{10}{\rm M}_\odot$) galaxies, with a rate of few $10^{-5}{\rm yr}^{-1}$, and an enhancement of up to 2 orders of magnitude compared to non-nucleated galaxies. We create a mock population of galaxies using different sets of scaling relations to explore trends with galaxy mass, taking into account the nucleated fraction of galaxies. Overall, we find a rate of few $10^{-5}{\rm yr}^{-1}$ which drops when galaxies are more massive than $10^{11}{\rm M}_\odot$ and contain MBHs swallowing stars whole and resulting in no observable TDE.
Counting Bounded Tree Depth Homomorphisms

We prove that graphs G, G' satisfy the same sentences of first-order logic with counting of quantifier rank at most k if and only if they are homomorphism-indistinguishable over the class of all graphs of tree depth at most k. Here G, G' are homomorphism-indistinguishable over a class F of graphs if for each graph F ϵ F, the number of homomorphisms from F to G equals the number of homomorphisms from F to G'.
Statistically Guided Divide-and-Conquer for Sparse Factorization of Large Matrix

The sparse factorization of a large matrix is fundamental in modern statistical learning. In particular, the sparse singular value decomposition and its variants have been utilized in multivariate regression, factor analysis, biclustering, vector time series modeling, among others. The appeal of this factorization is owing to its power in discovering a highly-interpretable latent association network, either between samples and variables or between responses and predictors. However, many existing methods are either ad hoc without a general performance guarantee, or are computationally intensive, rendering them unsuitable for large-scale studies. We formulate the statistical problem as a sparse factor regression and tackle it with a divide-and-conquer approach. In the first stage of division, we consider both sequential and parallel approaches for simplifying the task into a set of co-sparse unit-rank estimation (CURE) problems, and establish the statistical underpinnings of these commonly-adopted and yet poorly understood deflation methods. In the second stage of division, we innovate a contended stagewise learning technique, consisting of a sequence of simple incremental updates, to efficiently trace out the whole solution paths of CURE. Our algorithm has a much lower computational complexity than alternating convex search, and the choice of the step size enables a flexible and principled tradeoff between statistical accuracy and computational efficiency. Our work is among the first to enable stagewise learning for non-convex problems, and the idea can be applicable in many multi-convex problems. Extensive simulation studies and an application in genetics demonstrate the effectiveness and scalability of our approach.
STH: Spatio-Temporal Hybrid Convolution for Efficient Action Recognition

Effective and Efficient spatio-temporal modeling is essential for action recognition. Existing methods suffer from the trade-off between model performance and model complexity. In this paper, we present a novel Spatio-Temporal Hybrid Convolution Network (denoted as "STH") which simultaneously encodes spatial and temporal video information with a small parameter cost. Different from existing works that sequentially or parallelly extract spatial and temporal information with different convolutional layers, we divide the input channels into multiple groups and interleave the spatial and temporal operations in one convolutional layer, which deeply incorporates spatial and temporal clues. Such a design enables efficient spatio-temporal modeling and maintains a small model scale. STH-Conv is a general building block, which can be plugged into existing 2D CNN architectures such as ResNet and MobileNet by replacing the conventional 2D-Conv blocks (2D convolutions). STH network achieves competitive or even better performance than its competitors on benchmark datasets such as Something-Something (V1 & V2), Jester, and HMDB-51. Moreover, STH enjoys performance superiority over 3D CNNs while maintaining an even smaller parameter cost than 2D CNNs.
Toronto-3D: A Large-scale Mobile LiDAR Dataset for Semantic Segmentation of Urban Roadways

Semantic segmentation of large-scale outdoor point clouds is essential for urban scene understanding in various applications, especially autonomous driving and urban high-definition (HD) mapping. With rapid developments of mobile laser scanning (MLS) systems, massive point clouds are available for scene understanding, but publicly accessible large-scale labeled datasets, which are essential for developing learning-based methods, are still limited. This paper introduces Toronto-3D, a large-scale urban outdoor point cloud dataset acquired by a MLS system in Toronto, Canada for semantic segmentation. This dataset covers approximately 1 km of point clouds and consists of about 78.3 million points with 8 labeled object classes. Baseline experiments for semantic segmentation were conducted and the results confirmed the capability of this dataset to train deep learning models effectively. Toronto-3D is released 1 to encourage new research, and the labels will be improved and updated with feedback from the research community.
Fixing the train-test resolution discrepancy: FixEfficientNet

This note complements the paper "Fixing the train-test resolution discrepancy" that introduced the FixRes method. First, we show that this strategy is advantageously combined with recent training recipes from the literature. Most importantly, we provide new results for the EfficientNet architecture. The resulting network, called FixEfficientNet, significantly outperforms the initial architecture with the same number of parameters. For instance, our FixEfficientNet-B0 trained without additional training data achieves 79.3% top-1 accuracy on ImageNet with 5.3M parameters. This is a +0.5% absolute improvement over the Noisy student EfficientNet-B0 trained with 300M unlabeled images and +1.7% compared to the EfficientNet-B0 trained with adversarial examples. An EfficientNet-L2 pre-trained with weak supervision on 300M unlabeled images and further optimized with FixRes achieves 88.5% top-1 accuracy (top-5: 98.7%), which establishes the new state of the art for ImageNet with a single crop.This improvement in performance transfers to the experimental setting of ImageNet-v2, that is less prone to overfitting, for which we establish the new state of the art.