Miracle-dz/UniFAR-FTUs huggingface dataset:
Cinematic Techniques in Narrative Visualization

The many genres of narrative visualization (e.g. data comics, data videos) each offer a unique set of affordances and constraints. To better understand a genre that we call cinematic visualizations-3D visualizations that make highly deliberate use of a camera to convey a narrative-we gathered 50 examples and analyzed their traditional cinematic aspects to identify the benefits and limitations of the form. While the cinematic visualization approach can violate traditional rules of visualization, we find that through careful control of the camera, cinematic visualizations enable immersion in data-driven, anthropocentric environments, and can naturally incorporate in-situ narrators, concrete scales, and visual analogies. Our analysis guides our design of a series of cinematic visualizations, created for NASA's Earth Science Communications team. We present one as a case study to convey design guidelines covering cinematography, lighting, set design, and sound, and discuss challenges in creating cinematic visualizations.
Quantify How Space Mission Influence Geopolitical Dynamics? A Security and Social Policy Approach

We present a computational method to quantify the geopolitical impact of a space mission, based on the national budget and data logs of previous missions, and evidencing how even if some missions succeed, they can bring negative effects to the sponsored country. The objective of this research is to study how the success (or failure) of a space mission can bring an economical and political benefit (or loss) to a country. By retrieving various data, including sentiment from #hashtags related to the considered space missions, national budgets for space exploration, and the reliability of space launch systems, from social networks, public institutions, and online repositories, we propose an equation to evaluate the geopolitical importance of a space mission for a particular country or space agency. The geopolitical equation can be used by public institutions or private companies to estimate the potential impact of a space mission on public opinion and international relationships, which can be either positive or negative, as even successful missions may negatively affect international relationships and negotiations with some countries and their partners. Also we combine the ideology of classic social policy with a security and space mission point of view, to enlighten cultural, institutional, and political limits in public spending decisions.
Using a penalized likelihood to detect mortality deceleration

We suggest a novel method for detecting mortality deceleration by adding a penalty to the log-likelihood function in a gamma-Gompertz setting. This is an alternative to traditional likelihood inference and hypothesis testing. The main advantage of the proposed method is that it does not involve using a p-value, hypothesis testing, and asymptotic distributions. We evaluate the performance of our approach by comparing it with traditional likelihood inference on both simulated and real mortality data. Results have shown that our method is more accurate in detecting mortality deceleration and provides more reliable estimates of the underlying parameters. The proposed method is a significant contribution to the literature as it offers a powerful tool for analyzing mortality patterns.
Generalized Helmholtz Decomposition for Modal Analysis of Electromagnetic Problems in Inhomogeneous Media

Potential-based formulation with generalized Lorenz gauge can be used in the quantization of electromagnetic fields in inhomogeneous media. However, one often faces the redundancy of modes when finding eigenmodes from potential-based formulation. In free space, this can be explained by the connection to the well-known Helmholtz decomposition. In this work, we generalize the Helmholtz decomposition to its generalized form, echoing the use of generalized Lorenz gauge in inhomogeneous media. We formulate electromagnetics eigenvalue problems using vector potential formulation which is often used in numerical quantization. The properties of the differential operators are mathematically analyzed. Orthogonality relations between the two classes of modes are proved in both continuous and discrete space. Completeness of two sets of modes and the orthogonality relations are numerically validated in inhomogeneous anisotropic media. This work serves as a foundation for numerical quantization of electromagnetic fields in inhomogeneous media with potential-based formulation.
Analyzing Microarray Gene Expression Data

Statistical Estimation of Epidemiological Risk is a concise, organized, and well-written text that provides the derivations of statistical formulas underlying much epidemiological research and practice. The book is divided into eight chapters covering formulas for estimating population prevalence under different sampling designs and familiar risk estimators such as the relative risk, odds ratio, and attributable risk. In addition, the book covers less frequently used risk estimators, including the risk difference (the absolute difference in risk between two populations), the relative difference (excess effect due to an exposure or treatment), the generalized odds ratio (used for ordinal response variables), and the number needed to treat (the average number of persons who would require treatment to prevent one adverse outcome on average). Each section is organized similarly, covering estimation under binomial, cluster, and inverse sampling designs, and uses clear examples to illustrate the appropriate application of each method. There are ample references for readers interested in the original research motivating each method. Statistical exercises follow each chapter, most focusing on the derivation of formulas given in the text. There are two audiences for this text. The primary audience includes biostatisticians and statistically sophisticated epidemiologists, who will be interested in the mathematical derivations and have the necessary skills to apply the resulting formulas. The secondary audience is epidemiologic researchers, who will find the book a valuable addition to the classic text by Fleiss, Levin, and Paik (2003). For most epidemiologists who are not “current” with the biostatistical literature, this book can serve as a guide to the broad range of risk estimation statistics applied to both simple and complex study designs. An epidemiologist could use this text to make an informed selection of study design, knowing that statistical methods will subsequently be available for its analysis. An epidemiologist may also find the text useful while working with biostatisticians, both in directing a collaborating statistician to appropriate methods and in giving an explanation of methods that may not be readily accessible to epidemiologists.
Nearest Neighbor-Based Contrastive Learning for Hyperspectral and LiDAR Data Classification

The joint hyperspectral image (HSI) and light detection and ranging (LiDAR) data classification aims to interpret ground objects at more detailed and precise level. Although deep learning methods have shown remarkable success in the multisource data classification task, self-supervised learning has rarely been explored. It is commonly nontrivial to build a robust self-supervised learning model for multisource data classification, due to the fact that the semantic similarities of neighborhood regions are not exploited in the existing contrastive learning framework. Furthermore, the heterogeneous gap induced by the inconsistent distribution of multisource data impedes the classification performance. To overcome these disadvantages, we propose a nearest neighbor-based contrastive learning network (NNCNet), which takes full advantage of large amounts of unlabeled data to learn discriminative feature representations. Specifically, we propose a nearest neighbor-based data augmentation scheme to use enhanced semantic relationships among nearby regions. The intermodal semantic alignments can be captured more accurately. In addition, we design a bilinear attention module to exploit the second-order and even high-order feature interactions between the HSI and LiDAR data. Extensive experiments on four public datasets demonstrate the superiority of our NNCNet over state-of-the-art methods. The source codes are available at https://github.com/summitgao/NNCNet.
Massive Molecular Gas Companions Uncovered by Very Large Array CO(1–0) Observations of the z = 5.2 Radio Galaxy TN J0924−2201

We present Karl G. Jansky Very Large Array K-band (19 GHz) observations of the redshifted CO(1–0) line emission toward the radio galaxy TN J0924−2201 at z = 5.2, which is one of the most distant radio galaxies with CO detected. With an angular resolution of ∼2″, the CO(1–0) line emission is resolved into three clumps, within ±500 km s−1 relative to its redshift, which is determined by Lyα. We find that the clumps are located off-center and 12–33 kpc away from the center of the host galaxy, which has counterparts in the Hubble Space Telescope i band, Spitzer/IRAC, and the Atacama Large Millimeter/submillimeter Array (ALMA) band 6 (230 GHz; 1.3 mm). With the ALMA detection, we estimate the infrared luminosity L IR and the star formation rate (SFR) of the host galaxy to be (9.3 ± 1.7) × 1011 L ⊙ and 110 ± 20 M ⊙ yr−1, respectively. We also derive the 3σ upper limit of MH2<1.3×1010 M ⊙ at the host galaxy. The detected CO(1–0) line luminosities of the three clumps, L′CO(1−0) = (3.2–4.7) × 1010 K km s−1 pc2, indicate the presence of three massive molecular gas reservoirs, with MH2 = (2.5–3.7) × 1010 M ⊙, assuming a CO-to-H2 conversion factor of α CO = 0.8 M⊙(Kkms−1pc2)−1 , although the SFR is not elevated due to the nondetection of the ALMA 1.3 mm continuum (SFR < 40 M ⊙ yr−1). From the host galaxy, the nearest molecular gas clump, labeled “clump A,” is apparently in alignment with the radio jet axis, showing radio–CO alignment. The origins of these three clumps around TN J0924–2201 can possibly be interpreted as outflows, mergers, or jet-induced metal enrichment.
Fast and Correct Gradient-Based Optimisation for Probabilistic Programming via Smoothing

We study the foundations of variational inference, which frames posterior inference as an optimisation problem, for probabilistic programming. The dominant approach for optimisation in practice is stochastic gradient descent. In particular, a variant using the so-called reparameterisation gradient estimator exhibits fast convergence in a traditional statistics setting. Unfortunately, discontinuities, which are readily expressible in programming languages, can compromise the correctness of this approach. We consider a simple (higher-order, probabilistic) programming language with conditionals, and we endow our language with both a measurable and a smoothed (approximate) value semantics. We present type systems which establish technical pre-conditions. Thus we can prove stochastic gradient descent with the reparameterisation gradient estimator to be correct when applied to the smoothed problem. Besides, we can solve the original problem up to any error tolerance by choosing an accuracy coefficient suitably. Empirically we demonstrate that our approach has a similar convergence as a key competitor, but is simpler, faster, and attains orders of magnitude reduction in work-normalised variance.
The exact bootstrap mean and variance of an L‐estimator

Exact analytic expressions for the bootstrap mean and variance of any L‐estimator are obtained, thus eliminating the error due to bootstrap resampling. The expressions follow from the direct calculation of the bootstrap mean vector and covariance matrix of the whole set of order statistics. By using these expressions, recommendations can be made about the appropriateness of bootstrap estimation under given conditions.
Reinforcement Learning for Joint Optimization of Multiple Rewards

Finding optimal policies which maximize long term rewards of Markov Decision Processes requires the use of dynamic programming and backward induction to solve the Bellman optimality equation. However, many real-world problems require optimization of an objective that is non-linear in cumulative rewards for which dynamic programming cannot be applied directly. For example, in a resource allocation problem, one of the objectives is to maximize long-term fairness among the users. We notice that when an agent aim to optimize some function of the sum of rewards is considered, the problem loses its Markov nature. This paper addresses and formalizes the problem of optimizing a non-linear function of the long term average of rewards. We propose model-based and model-free algorithms to learn the policy, where the model-based policy is shown to achieve a regret of $\Tilde{O}\left(LKDS\sqrt{\frac{A}{T}}\right)$ for $K$ objectives combined with a concave $L$-Lipschitz function. Further, using the fairness in cellular base-station scheduling, and queueing system scheduling as examples, the proposed algorithm is shown to significantly outperform the conventional RL approaches.
Energy conversion efficiency with soliton fission

We formulate the energy conversion efficiency from a high-order soliton to fundamental solitons by including the influence of interpulse Raman scattering in the fission process. The proposed analytical formula agrees closely with numerical results of the generalized nonlinear Schrodinger equation as well as to experimental results, while the resulting formulation significantly alters the energy conversion efficiency predicted by the Raman-independent inverse scattering method. We also calculate the energy conversion efficiency in materials of different Raman gain profiles such as silica, ZBLAN and chalcogenide glasses (As2S3 and As2Se3). It is predicted that ZBLAN glass leads to the largest energy conversion efficiency of all four materials. The energy conversion efficiency is a notion of utmost practical interest for the design of wavelength converters and supercontinuum generation systems based on the dynamics of soliton self-frequency shift.
Mining Seasonal Temporal Patterns in Time Series

As IoT-enabled sensors become more pervasive, very large time series data are increasingly generated and made available for advanced data analytics. By mining temporal patterns from the available data, valuable insights can be extracted to support decision making. A useful type of patterns found in many real-world applications exhibits periodic occurrences, and is thus called seasonal temporal patterns (STP). Compared to regular patterns, mining seasonal temporal patterns is more challenging since traditional measures such as support and confidence do not capture the seasonality characteristics. Further, the anti-monotonicity property does not hold for STPs, and thus, resulting in an exponential search space. We propose a first solution for seasonal temporal pattern mining (STPM) from time series that can mine STP at different data granularities. We design efficient data structures and use two pruning techniques for the STPM algorithm that downsize the search space and accelerate the mining process. Further, based on the mutual information measure, we propose an approximate version of STPM that only mine seasonal patterns on the promising time series. Finally, extensive experiments with real-world and synthetic datasets show that STPM outperforms the baseline in terms of runtime and memory usage, and can scale to large datasets. The approximate STPM is up to an order of magnitude faster and less memory-consuming than the baseline, while maintaining high accuracy.
Genetic similarity versus genetic ancestry groups as sample descriptors in human genetics

A common sample descriptor in human genomics studies is that of 'genetic ancestry group', with terms such as 'European genetic ancestry' or 'East Asian genetic ancestry' frequently used in publications to describe the genetics of groups of individuals based on the analysis of their genotypes. In this Perspective, I argue that these terms are imprecise and potentially misleading and that, for most applications, simple statements of genetic similarity represent a more accurate description.
Comparison of different models for EBCD calculation in the TJ-II Stellarator

In this work, we have compared different linear methods to estimate the electron Bernstein current drive (EBCD). The expressions for the current drive efficiency have been plugged into the ray tracing code TRUBA, which was used in previous works for electron Bernstein wave (EBW) heating studies in the TJ-II stellarator. This device is taken here as an example for this comparison. The driven current is calculated for different densities and temperatures, as well as launching directions of the heating beam, which is a critical issue in the O–X–B mode conversion scenario considered in TJ-II. The range of applicability of each model is discussed. The influence of the Ohkawa, relativistic and frictional trapping effects on the total current generated is studied by comparing the results obtained by pairs of models that include and neglect those effects. The Ohkawa effect has resulted in being the least important. Although the relativistic effects are not negligible, the main disagreement between the results arises from including or not momentum conservation and neglecting frictional trapping effects. The total EBCD current drive efficiency calculated is in all cases greater than the experimental ECCD one, previously measured in TJ-II. The results presented in this work are the guideline for future experiments in this device.
TECHNIQUES TO ENHANCE FLUID MICRO-MIXING AND CHAOTIC MICROMIXERS

With fast development of microfluidic systems, fluid micro-mixing becomes a very important issue. In this paper, recent developments on various micromixers and their working mechanisms are reviewed, including the external agitation methods applied in active mixing and the channel geometries adopted in passive mixer design. The chaotic mixing and the influences of Re would be mainly discussed. At moderate and high Re, the fluid inertial effects usually facilitate the chaotic mixing. At low Re, generation of chaotic advection becomes more difficult but can still be achieved through fluid manipulations such as stretching and folding. Chaotic mixers can be characterized using dynamical system techniques, such as Poincare plot, and Lyapunov exponent.
Error estimates in Gaussian quadrature for functions of bounded variation

In this paper, for quadrature formulae $Q_n$ and their respective remainder terms $R_n$, error estimates are considered for the form \[ R_n [f] \leq \varrho _V (R_n )V(f),\] where $V(f)$ is the total variation over the interval of integration of the function f, and $\varrho _V (R_n )$ is the best possible error constant for $R_n $, i.e., \[ \varrho _V (R_n ) = \sup \{ | R_n [f] ||V(f) \leqslant 1 \} \]. For this class BV of functions of low-order continuity, the quality of positive quadrature formulae $Q_n $ having high algebraic degree of exactness is investigated. In particular, for the Gaussian formulae $Q_n^G $, simple explicit values of their error constants $\varrho _V (R_n^G )$ are stated, and it is shown that these differ from those of the respective optimal formulae only by a factor of less than ${\pi / 2}$. This result is based on an improvement of the classical separation theorem of Chebyshev–Markov–Stieltjes.
Multivariate spatial interpolation and exposure to air pollutants

We develop and apply an approach to the spatial interpolation of a vector-valued random response field. The Bayesian approach we adopt enables uncertainty about the underlying models to be representes in expressing the accuracy of the resulting interpolants. The methodology is particularly relevant in environmetrics, where vector-valued responses are only observed at designated sites at successive time points. The theory allows space-time modelling at the second level of the hierarchical prior model so that uncertainty about the model parameters has been fully expressed at the first level. In this way, we avoid unduly optimistic estimates of inferential accuracy. Moreover, the prior model can be upgraded with any available new data, while past data can be used in a systematic way to fit model parameters. The theory is based on the multivariate normal and related joint distributions. Our hierarchical prior models lead to posterior distributions which are robust with respect to the choice of the prior (hyperparameters). We illustrate our theory with an example involving monitoring stations in southern Ontario, where monthly average levels of ozone, sulphate, and nitrate are available and between-station response triplets are interpolated. In this example we use a recently developed method for interpolating spatial correlation fields. Dans ce rapport, nous developpons et appliquons une approche pour l'interpolation spatiale d'un champs de reponses vectorielles aleatoires. L'approche Bayesienne que nous adoptons permet de representer l'incertitude des modeles par l'expression de la precision des interpollants qui en decoulent. La methodologie est particulierement pertinente en environmetrie ou les reponses vectorielles sont seulement observees a des sites designes et a des temps successifs. La theorie permet une modelisation espace/temps au second niveau du modele hierarchique a priori de telle sorte que l'incertitude a propos des parametres soient pleinement exprimee au premier niveau. De cette facon, nous evitons d'avoir des estimes indument optimistes quant a la precision de ce qui est infere. De plus le modele a priori peut etre ameliore par tout nouvel ensemble de donnees lorsque les donnees passees peuvent etre utilisees d'une facon systematique pour ajuster les parametres du modele. La theorie est basee sur la normale mullidimensionnelle et les distributions conjointes associees. Notre modele hierarchique a priori nous conduit a des distributions a posteriori que sont robustes par rapport au choix des hyperparametres a priori. Nous illustrons notre theorie par un exemple impliquant des stations de controle dans le sud de l'Ontario ou des moyennes mensuelles des niveaux d'ozone, de sulfate et de nitrate sont disponibles, et les triplets reponses entre les stations sont interpoles. Dans cet exemple nous utilisons une methode recemment developpee pour l'interpolation de champs de correlation spatiale.
Cross-coupled bandpass filters using QMSIW cavities and S-shaped slot coupling structures

This paper presents a novel cross-coupled bandpass filter using quarter-mode substrate integrated waveguide (QMSIW) cavities. QMSIWs with fictitious magnetic walls on two sides retain similar performances of the conventional SIW, but are almost one fourth the size. The dominant resonant mode of the proposed resonator is the TE101 mode. A special negative coupling scheme with symmetrical S-shaped slots on the top and bottom metal planes connected by metallic vias is developed. The proposed structure provides more design flexibility in arranging the pitch of vias owing to the extended slot length. The experimental filter has a measured fractional bandwidth of 7.44% at 5.57 GHz with a return loss better than −18.1 dB and an insertion loss less than −2 dB.