Miracle-dz/UniFAR-FTUs huggingface dataset:
COVID-19 Epidemic in Mumbai: Projections, full economic opening, and containment zones versus contact tracing and testing: An Update

Mumbai, amongst the most densely populated cities in the world, has witnessed the fourth largest number of cases and the largest number of deaths among all the cities in India (as of 28th October 2020). Along with the rest of India, lockdowns (of varying degrees) have been in effect in Mumbai since March 25, 2020. Given the large economic toll on the country from the lockdown and the related restrictions on mobility of people and goods, swift opening of the economy especially in a financial hub such as Mumbai becomes critical. In this report, we use the IISc-TIFR agent based simulator to develop long term projections for Mumbai under realistic scenarios related to Mumbai's opening of the workplaces, or equivalently, the economy, and the associated public transportation through local trains and buses. These projections were developed taking into account a possible second wave if the economy and the local trains are fully opened either on November 1, 2020 or on January 1, 2021. The impact on infection spread in Mumbai if the schools and colleges open on January first week 2021 is also considered. We also try to account for the increased intermingling amongst the population during the Ganeshotsav festival as well as around the Navratri/Dussehra and Diwali festival. Our conclusion, based on our simulations, is that the impact of fully opening up the economy on November 1 is manageable provided reasonable medical infrastructure is in place. Further, schools and colleges opening in January do not lead to excessive increase in infections. The report also explores the relative effectiveness of contact tracing vs containment zones, and also includes very rudimentary results of the effect of vaccinating the elderly population in February 2021.
Identity and Personhood in Digital Democracy: Evaluating Inclusion, Equality, Security, and Privacy in Pseudonym Parties and Other Proofs of Personhood

Digital identity seems like a prerequisite for digital democracy: how can we ensure "one person, one vote" online without identifying voters? But digital identity solutions - ID checking, biometrics, self-sovereign identity, and trust networks - all present flaws, leaving users vulnerable to exclusion, identity loss or theft, and coercion. These flaws may be insurmountable because digital identity is a cart pulling the horse. We cannot achieve digital identity secure enough for the weight of digital democracy, until we build it on a solid foundation of "digital personhood. " While identity is about distinguishing one person from another through attributes or affiliations, personhood is about giving all real people inalienable digital participation rights independent of identity, including protection against erosion of their democratic rights through identity loss, theft, coercion, or fakery. We explore and analyze alternative approaches to "proof of personhood" that may provide this missing foundation. Pseudonym parties marry the transparency of periodic physical-world events with the power of digital tokens between events. These tokens represent limited-term but renewable claims usable for purposes such as online voting or liquid democracy, sampled juries or deliberative polls, abuse-resistant social communication, or minting universal basic income in a permissionless cryptocurrency. Enhancing pseudonym parties to provide participants a moment of enforced physical security and privacy can address coercion and vote-buying risks that plague today's E-voting systems. We also examine other proposed approaches to proof of personhood, some of which offer conveniences such as all-online participation. These alternatives currently fall short of satisfying all the key digital personhood goals, unfortunately, but offer valuable insights into the challenges we face.
Video Generative Adversarial Networks: A Review

With the increasing interest in the content creation field in multiple sectors such as media, education, and entertainment, there is an increased trend in the papers that use AI algorithms to generate content such as images, videos, audio, and text. Generative Adversarial Networks (GANs) is one of the promising models that synthesizes data samples that are similar to real data samples. While the variations of GANs models in general have been covered to some extent in several survey papers, to the best of our knowledge, this is the first paper that reviews the state-of-the-art video GANs models. This paper first categorizes GANs review papers into general GANs review papers, image GANs review papers, and special field GANs review papers such as anomaly detection, medical imaging, or cybersecurity. The paper then summarizes the main improvements in GANs that are not necessarily applied in the video domain in the first run but have been adopted in multiple video GANs variations. Then, a comprehensive review of video GANs models are provided under two main divisions based on existence of a condition. The conditional models are then further classified according to the provided condition into audio, text, video, and image. The paper concludes with the main challenges and limitations of the current video GANs models.
Assessing Road Traffic Safety Under COVID-19: Inequality, Irregularity, and Severity

COVID-19 has been affecting every social sector significantly, including human mobility and subsequently road traffic safety. In this study, we analyze the impact of the pandemic on traffic accidents safety using two cities, namely Los Angeles and New York City in the U.S., as examples. Specifically, we have analyzed traffic accidents associated with various demographic groups, how traffic accidents are distributed in time and space, and the severity level of traffic accidents that both involve and not involve other transportation modes (e.g., pedestrians and motorists). We have made the following observations: 1) the pandemic has been affecting demographic groups categorized in age, race, and gender unequally; 2) the “hotspots” of traffic accidents have been shifted in both time and space compared to time periods that are prior to the pandemic, demonstrating irregularity; and 3) the number of non-fatal accident cases has reduced but the number of severe and fatal cases of traffic accidents remains the same under the pandemic.
Unveiling temperature dependence mechanisms of perpendicular magnetic anisotropy at Fe/MgO interfaces.

The perpendicular magnetic anisotropy (PMA) at magnetic transition metal/oxide interfaces is a key element in building out-of-plane magnetized magnetic tunnel junctions for spin-transfer-torque magnetic random access memory (STT-MRAM). Size downscaling renders magnetic properties more sensitive to thermal effects. Thus, understanding temperature dependence of magnetic anisotropy becomes crucial. In this work, we theoretically address the correlation between temperature dependence of PMA and magnetization in typical Fe/MgO-based structures. In particular, the possible mechanisms behind experimentally reported deviations from the Callen and Callen scaling power law are analyzed. First-principles calculations reveal small high-order anisotropy terms ruling out an intrinsic microscopic mechanism underlying those deviations. Neglecting higher-order anisotropy terms in the atomisitic spin Hamiltonian, two possible extrinsic macroscopic mechanisms are unveiled: influence of the dead layer, always present in storage layer of STT-MRAM cells, and spatial inhomogeneities of interfacial magnetic anisotropy. We show that presence of a dead layer simultaneously with scaling the anisotropy constant by the total magnetization of the sample rather than that of the interface itself lead to low scaling powers. In the second mechanism, increasing the percentage of inhomogeneity in the interfacial PMA is revealed to decrease the scaling power. Apart from those different mechanisms, the layer-resolved temperature-dependence of PMA is shown to ideally follow the Callen and Callen scaling power law for each individual Fe layer. These results allow coherently explaining the difference in scaling powers relating anisotropy and magnetization thermal variations reported in earlier experiments. This is crucial for the understanding of the thermal stability of the storage layer magnetization in STT-MRAM applications.
Insights into Ordinal Embedding Algorithms: A Systematic Evaluation

The objective of ordinal embedding is to find a Euclidean representation of a set of abstract items, using only answers to triplet comparisons of the form "Is item $i$ closer to the item $j$ or item $k$? ". In recent years, numerous algorithms have been proposed to solve this problem. However, there does not exist a fair and thorough assessment of these embedding methods and therefore several key questions remain unanswered: Which algorithms scale better with increasing sample size or dimension? Which ones perform better when the embedding dimension is small or few triplet comparisons are available? In our paper, we address these questions and provide the first comprehensive and systematic empirical evaluation of existing algorithms as well as a new neural network approach. In the large triplet regime, we find that simple, relatively unknown, non-convex methods consistently outperform all other algorithms, including elaborate approaches based on neural networks or landmark approaches. This finding can be explained by our insight that many of the non-convex optimization approaches do not suffer from local optima. In the low triplet regime, our neural network approach is either competitive or significantly outperforms all the other methods. Our comprehensive assessment is enabled by our unified library of popular embedding algorithms that leverages GPU resources and allows for fast and accurate embeddings of millions of data points.
Cooperative Multi-Beam Routing for Multi-IRS Aided Massive MIMO

Intelligent reflecting surface (IRS) is envisioned to play a significant role in future wireless communication systems thanks to its powerful capability of enabling smart and reconfigurable radio environment. In this paper, we study the multi-IRS aided downlink communication in a massive multiple-input multiple-output (MIMO) system, where a multi-antenna BS simultaneously serves multiple remote single-antenna users with orthogonal beams reflected by multiple IRSs. By exploiting the line-of-sight (LoS) link between each pair of selected IRSs, a multi-hop cascaded LoS link can be established between the BS and each user via their cooperative beam routing. Under this setup, we optimize the selected IRSs and their beam routing path for each user, along with the BS/IRS active/passive beamforming such that the minimum received signal power among all users is maximized, subject to a new multi-beam routing path separation constraint for avoiding the inter-user/route interference. To tackle this problem, we first derive the optimal BS/IRS active/passive beamforming in closed-form for any given beam routes and show the beam routing optimization is NP-complete by recasting it as an equivalent graph-optimization problem. To solve this challenging problem, we then propose an efficient recursive algorithm to partially enumerate the feasible solutions, which effectively balances the performance-complexity trade-off by tuning its design parameter. Numerical results demonstrate that the proposed algorithm can achieve near-optimal performance with low enumeration complexity and also outperform other benchmark schemes.
Registration Loss Learning for Deep Probabilistic Point Set Registration

Probabilistic methods for point set registration have interesting theoretical properties, such as linear complexity in the number of used points, and they easily generalize to joint registration of multiple point sets. In this work, we improve their recognition performance to match state of the art. This is done by incorporating learned features, by adding a von Mises-Fisher feature model in each mixture component, and by using learned attention weights. We learn these jointly using a registration loss learning strategy (RLL) that directly uses the registration error as a loss, by back-propagating through the registration iterations. This is possible as the probabilistic registration is fully differentiable, and the result is a learning framework that is truly end-to-end. We perform extensive experiments on the 3DMatch and Kitti datasets. The experiments demonstrate that our approach benefits significantly from the integration of the learned features and our learning strategy, outperforming the state-of-the-art on Kitti. Code is available at https://github.com/felja633/RLLReg.
Autonomous Wall Building with a UGV- UAV Team at MBZIRC 2020

Constructing large structures with robots is a challenging task with many potential applications that requires mobile manipulation capabilities. We present two systems for autonomous wall building that we developed for the Mohamed Bin Zayed International Robotics Challenge 2020. Both systems autonomously perceive their environment, find bricks, and build a predefined wall structure. While the UGV uses a 3D LiDAR-based perception system which measures brick poses with high precision, the UAV employs a real-time camera-based system for visual servoing. We report results and insights from our successful participation at the MBZIRC 2020 Finals, additional lab experiments, and discuss the lessons learned from the competition.
On the Moral Justification of Statistical Parity

A crucial but often neglected aspect of algorithmic fairness is the question of how we justify enforcing a certain fairness metric from a moral perspective. When fairness metrics are proposed, they are typically argued for by highlighting their mathematical properties. Rarely are the moral assumptions beneath the metric explained. Our aim in this paper is to consider the moral aspects associated with the statistical fairness criterion of independence (statistical parity). To this end, we consider previous work, which discusses the two worldviews "What You See Is What You Get" (WYSIWYG) and "We're All Equal" (WAE) and by doing so provides some guidance for clarifying the possible assumptions in the design of algorithms. We present an extension of this work, which centers on morality. The most natural moral extension is that independence needs to be fulfilled if and only if differences in predictive features (e.g. high school grades and standardized test scores are predictive of performance at university) between socio-demographic groups are caused by unjust social disparities or measurement errors. Through two counterexamples, we demonstrate that this extension is not universally true. This means that the question of whether independence should be used or not cannot be satisfactorily answered by only considering the justness of differences in the predictive features.
Dynamical spin correlations of the kagome antiferromagnet

Temperature-dependent dynamical spin correlations, which can be readily accessed via a variety of experimental techniques, hold the potential of offering a unique fingerprint of quantum spin liquids and other intriguing dynamical states. In this work we present an in-depth study of the temperature-dependent dynamical spin structure factor $S(\mathbf{q},\ensuremath{\omega})$ of the antiferromagnetic (AFM) Heisenberg spin-1/2 model on the kagome lattice with additional Dzyaloshinskii-Moriya (DM) interactions. Using the finite-temperature Lanczos method on lattices with up to $N=30$ sites we find that even without DM interactions, chiral low-energy spin fluctuations of the ${120}^{\ensuremath{\circ}}$ AFM order parameter dominate the dynamical response. This leads to a nontrivial frequency dependence of $S(\mathbf{q},\ensuremath{\omega})$ and the appearance of a pronounced low-frequency mode at the M point of the extended Brillouin zone. Adding an out-of-plane DM interactions ${D}^{z}$ gives rise to an anisotropic dynamical response, a softening of in-plane spin fluctuations, and, ultimately, the onset of a coplanar AFM ground-state order at ${D}^{z}g0.1J$. Our results are in very good agreement with existing inelastic neutron scattering and temperature-dependent NMR spin-lattice relaxation rate $(1/{T}_{1})$ data on the paradigmatic kagome AFM compound herbertsmithite, where the effect of its small ${D}^{z}$ on the dynamical spin correlations is shown to be rather small, as well as with $1/{T}_{1}$ data on the novel kagome AFM compound ${\mathrm{YCu}}_{3}{(\mathrm{OH})}_{6}{\mathrm{Cl}}_{3}$, where its substantial ${D}^{z}\ensuremath{\approx}0.25J$ interaction is found to strongly affect its spin dynamics.
Differential Modeling Systematics across the HR Diagram from Asteroseismic Surface Corrections

Localized modeling error in the near-surface layers of evolutionary stellar models causes the frequencies of their normal modes of oscillation to differ from those of actual stars with matching interior structures. These frequency differences are referred to as the asteroseismic surface term. Global stellar properties estimated via detailed constraints on individual mode frequencies have previously been shown to be robust with respect to different parameterizations of this surface term. It has also been suggested that this may be true of a broader class of nonparametric treatments. We examine systematic differences in inferred stellar properties with respect to different surface-term treatments, both for a statistically large sample of main-sequence stars, as well as for a sample of red giants, for which no such characterization has previously been done. For main-sequence stars, we demonstrate that while masses and radii, and hence ages, are indeed robust to the choice of surface term, the inferred initial helium abundance Y0 is sensitive to the choice of surface correction. This implies that helium-abundance estimates returned from detailed asteroseismology are methodology dependent. On the other hand, for our red giant sample, nonparametric surface corrections return dramatically different inferred stellar properties than parametric ones do. The nature of these differences suggests that such nonparametric methods should be preferred for evolved stars; this should be verified on a larger sample.
Wiretap Channels With Causal and Non-Causal State Information: Revisited

The coding problem for wiretap channels (WTCs) with <italic>causal</italic> and/or <italic>non-causal</italic> channel state information (CSI) available at the encoder (Alice) and/or the decoder (Bob) is studied, particularly focusing on achievable secret-message secret-key (SM-SK) rate pairs under the <italic>semantic security</italic> criterion. One of our main results is summarized as <xref ref-type="theorem" rid="theorem3">Theorem 3</xref> on causal inner bounds for SM-SK rate pairs, which follows immediately by leveraging the unified seminal theorem for WTCs with <italic>non-causal</italic> CSI at Alice that has been recently established by Bunin <italic>et al.</italic>. The only thing to do here is just to re-interpret the latter non-causal scheme in a causal manner by restricting the range of auxiliary random variables appearing in non-causal encoding to a subclass of auxiliary random variables for the causal encoder. This technique is referred to as “plugging. ” Then, we are able to dispense with the block-Markov encoding scheme used in the previous works by Chia and El Gamal, Fujita, and Han and Sasaki and then extend all the known results on achievable rates. The other main results include the exact SM-SK capacity region for WTCs with non-causal CSI at “both” Alice and Bob (<xref ref-type="theorem" rid="theorem2">Theorem 2</xref>), a “tighter” causal SM-SK outer bound for state-reproducing coding schemes with CSI at Alice (<xref ref-type="other" rid="other4">Proposition 4</xref>), and the exact SM-SK capacity region for degraded WTCs with causal/non-causal CSI at both Alice and Bob (<xref ref-type="theorem" rid="theorem4">Theorem 4</xref>).
2-Level Quasi-Planarity or How Caterpillars Climb (SPQR-)Trees

Given a bipartite graph $G=(V_b,V_r,E)$, the $2$-Level Quasi-Planarity problem asks for the existence of a drawing of $G$ in the plane such that the vertices in $V_b$ and in $V_r$ lie along two parallel lines $\ell_b$ and $\ell_r$, respectively, each edge in $E$ is drawn in the unbounded strip of the plane delimited by $\ell_b$ and $\ell_r$, and no three edges in $E$ pairwise cross. We prove that the $2$-Level Quasi-Planarity problem is NP-complete. This answers an open question of Dujmovi\'c, P\'{o}r, and Wood. Furthermore, we show that the problem becomes linear-time solvable if the ordering of the vertices in $V_b$ along $\ell_b$ is prescribed. Our contributions provide the first results on the computational complexity of recognizing quasi-planar graphs, which is a long-standing open question. Our linear-time algorithm exploits several ingredients, including a combinatorial characterization of the positive instances of the problem in terms of the existence of a planar embedding with a caterpillar-like structure, and an SPQR-tree-based algorithm for testing the existence of such a planar embedding. Our algorithm builds upon a classification of the types of embeddings with respect to the structure of the portion of the caterpillar they contain and performs a computation of the realizable embedding types based on a succinct description of their features by means of constant-size gadgets.
Abel maps for nodal curves via tropical geometry

We consider Abel maps for regular smoothing of nodal curves with values in the Esteves compactified Jacobian. In general, these maps are just rational, and an interesting question is to find an explicit resolution. We translate this problem into an explicit combinatorial problem by means of tropical and toric geometry. We show that the solution of the combinatorial problem gives rise to an explicit resolution of the Abel map. We are able to use this technique to construct and study all the Abel maps of degree one.
Awareness Toward COVID-19 Precautions Among Different Levels of Dental Students in King Saud University, Riyadh, Saudi Arabia

Purpose The risk of acquiring coronavirus disease 2019 (COVID-19) among dental professionals is high because they are exposed to aerosol production and body fluids during dental procedures. Therefore, the aim of the study was to determine the level of awareness among the dental students regarding COVID-19. Materials and Methods An online cross-sectional questionnaire with three sections (knowledge, attitudes, practice) was completed by 412 postgraduate and undergraduate dental students and interns from King Saud University in Saudi Arabia. Results The overall mean survey score was 9.56±1.19 out of 15. A mean score of 2.31±0.32 was recorded for level of knowledge among the dental professionals that indicated a fair level of knowledge among the participants. Similarly, a mean score of 6.5±0.45 was recorded for attitude of dental professionals towards COVID-19 that indicated a moderate level of attitude. “Novel COVID-19 virus transmits through droplets,” and “masks should be worn most of the time to prevent infection spread,” had the lowest correct answer rates. The practice had a low mean score of 0.81±0.22. The results depicted statistically significant differences in knowledge, with postgraduate students having the highest mean scores, followed by interns and undergraduate students. Conclusion Despite attaining fair scores on knowledge and attitudes, the low practice scores highlight the need for urgent strategies to prevent infection among dental students, including mandatory crash courses and hands-on protection measures.