Miracle-dz/UniFAR-FTUs huggingface dataset:
Alfvénic versus non-Alfvénic turbulence in the inner heliosphere as observed by Parker Solar Probe

<p>Parker Solar Probe (PSP) measures the magnetic field and plasma parameters of the solar wind at unprecedentedly close distances to the Sun, providing a great opportunity to study the early-stage evolution of magnetohydrodynamic (MHD) turbulence in the solar wind. Here we use PSP data to explore the nature of solar wind turbulence focusing on the Alfv&#233;nic character and power spectra of the fluctuations and their dependence on heliocentric distance and context (i.e., large-scale solar wind properties), aiming to understand the role that different effects such as source properties, solar wind expansion, and stream interaction might play in determining the turbulent state. We carried out a statistical survey of the data from the first five orbits of PSP with a focus on how the fluctuation properties at the large MHD scales vary with different solar wind streams and the distance from the Sun. A more in-depth analysis from several selected periods is also presented. Our results show that as fluctuations are transported outward by the solar wind, the magnetic field spectrum steepens while the shape of the velocity spectrum remains unchanged. The steepening process is controlled by the age of the turbulence, which is determined by the wind speed together with the radial distance. Statistically, faster solar wind has higher Alfv&#233;nicity with a more dominant outward propagating wave component and more balanced magnetic and kinetic energies. The outward wave dominance gradually weakens with radial distance, while the excess of magnetic energy is found to be stronger as we move closer toward the Sun. We show that the turbulence properties can significantly vary from stream to stream even if these streams are of a similar speed, indicating very different origins of these streams. Especially, the slow wind that originates near the polar coronal holes has much lower Alfv&#233;nicity compared with the slow wind that originates from the active regions and pseudostreamers. We show that structures such as the heliospheric current sheet and wind stream velocity shears can play an important role in modifying the properties of the turbulence. </p><p>*The PSP Team:&#160;Stuart D.Bale, &#160;Justin Kasper, Kelly Korreck, J. W. Bonnell, Thierry Dudok de Wit, Keith Goetz, Peter R. Harvey, Robert J. MacDowall, David Malaspina, Marc Pulupa, Anthony W.Case, Davin Larson, &#160;Jenny Verniero, Roberto Livi, Michael Stevens, PhyllisWhittlesey, Milan Maksimovic, and Michel Moncuquet</p>
Random Embeddings with Optimal Accuracy

This work constructs Jonson-Lindenstrauss embeddings with best accuracy, as measured by variance, mean-squared error and exponential concentration of the length distortion. Lower bounds for any data and embedding dimensions are determined, and accompanied by matching and efficiently samplable constructions (built on orthogonal matrices). Novel techniques: a unit sphere parametrization, the use of singular-value latent variables and Schur-convexity are of independent interest.
Interface Features and Users' Well-Being: Measuring the Sensitivity of Users' Well-Being to Resource Constraints and Feature Types

Users increasingly face multiple interface features on one hand, and constraints on available resources (e.g., time, attention) on the other. Understanding the sensitivity of users’ well-being to feature type and resource constraints, is critical for informed design. Building on microeconomic theory, and focusing on social information features, users’ interface choices were conceptualized as an exchange of resources (e.g., time), in return for access to goods (social information features). We studied how sensitive users’ well-being is to features’ type, and to their cost level and type. We found that (1) increased cost of feature use leads to decreased well-being, (2) users’ well-being is a function of features’ cost type, and (3) users’ well-being is sensitive to differences in feature type. The approach used here to quantify user well-being derived from interface features offers a basis for asynchronous feature comparison. Author
Relation between AGN type and host galaxy properties

We use 3213 active galactic nuclei (AGNs) from the XMM-XXL northern field to investigate the relation of AGN type with host galaxy properties. Applying a Bayesian method, we derive the hardness ratios, and through these the hydrogen column density (NH) for each source. We consider those with NH > 1021.5 cm−2 as absorbed sources (type 2). We examine the star formation rate (SFR) and the stellar mass (M*) distributions for both absorbed and unabsorbed sources. Our work indicates that there is no significant link between AGN type and these host galaxy properties. Next, we investigate whether the AGN power, as represented by its X-ray luminosity (LX) correlates with any deviation of the host galaxy’s place from the so-called main sequence of galaxies, and we examine this separately for the obscured and the unobscured AGN populations. To take into account the effect of M* and redshift on SFR, we use the normalised SFR (SFRnorm). We find that the correlation between LX and SFRnorm follows approximately the same trend for both absorbed and unabsorbed sources, a result that favours the standard AGN unification models. Finally, we explore the connection between the obscuration (NH) and the SFR. We find that there is no relation between them, suggesting that obscuration is not related to the large-scale SFR in the galaxy.
Multitask Learning for Class-Imbalanced Discourse Classification

Small class-imbalanced datasets, common in many high-level semantic tasks like discourse analysis, present a particular challenge to current deep-learning architectures. In this work, we perform an extensive analysis on sentence-level classification approaches for the News Discourse dataset, one of the largest high-level semantic discourse datasets recently published. We show that a multitask approach can improve 7% Micro F1-score upon current state-of-the-art benchmarks, due in part to label corrections across tasks, which improve performance for underrepresented classes. We also offer a comparative review of additional techniques proposed to address resource-poor problems in NLP, and show that none of these approaches can improve classification accuracy in such a setting.
A Provably Efficient Algorithm for Linear Markov Decision Process with Low Switching Cost

Many real-world applications, such as those in medical domains, recommendation systems, etc, can be formulated as large state space reinforcement learning problems with only a small budget of the number of policy changes, i.e., low switching cost. This paper focuses on the linear Markov Decision Process (MDP) recently studied in [Yang et al 2019, Jin et al 2020] where the linear function approximation is used for generalization on the large state space. We present the first algorithm for linear MDP with a low switching cost. Our algorithm achieves an $\widetilde{O}\left(\sqrt{d^3H^4K}\right)$ regret bound with a near-optimal $O\left(d H\log K\right)$ global switching cost where $d$ is the feature dimension, $H$ is the planning horizon and $K$ is the number of episodes the agent plays. Our regret bound matches the best existing polynomial algorithm by [Jin et al 2020] and our switching cost is exponentially smaller than theirs. When specialized to tabular MDP, our switching cost bound improves those in [Bai et al 2019, Zhang et al 20020]. We complement our positive result with an $\Omega\left(dH/\log d\right)$ global switching cost lower bound for any no-regret algorithm.
Stereo Correspondence and Reconstruction of Endoscopic Data Challenge

The stereo correspondence and reconstruction of endoscopic data sub-challenge was organized during the Endovis challenge at MICCAI 2019 in Shenzhen, China. The task was to perform dense depth estimation using 7 training datasets and 2 test sets of structured light data captured using porcine cadavers. These were provided by a team at Intuitive Surgical. 10 teams participated in the challenge day. This paper contains 3 additional methods which were submitted after the challenge finished as well as a supplemental section from these teams on issues they found with the dataset.
Understanding Health Misinformation Transmission: An Interpretable Deep Learning Approach to Manage Infodemics

Health misinformation on social media devastates physical and mental health, invalidates health gains, and potentially costs lives. Understanding how health misinformation is transmitted is an urgent goal for researchers, social media platforms, health sectors, and policymakers to mitigate those ramifications. Deep learning methods have been deployed to predict the spread of misinformation. While achieving the state-of-the-art predictive performance, deep learning methods lack the interpretability due to their blackbox nature. To remedy this gap, this study proposes a novel interpretable deep learning approach, Generative Adversarial Network based Piecewise Wide and Attention Deep Learning (GAN-PiWAD), to predict health misinformation transmission in social media. Improving upon state-of-the-art interpretable methods, GAN-PiWAD captures the interactions among multi-modal data, offers unbiased estimation of the total effect of each feature, and models the dynamic total effect of each feature when its value varies. We select features according to social exchange theory and evaluate GAN-PiWAD on 4,445 misinformation videos. The proposed approach outperformed strong benchmarks. Interpretation of GAN-PiWAD indicates video description, negative video content, and channel credibility are key features that drive viral transmission of misinformation. This study contributes to IS with a novel interpretable deep learning method that is generalizable to understand other human decision factors. Our findings provide direct implications for social media platforms and policymakers to design proactive interventions to identify misinformation, control transmissions, and manage infodemics.
Modularity maximisation for graphons

Networks are a widely-used tool to investigate the large-scale connectivity structure in complex systems and graphons have been proposed as an infinite size limit of dense networks. The detection of communities or other meso-scale structures is a prominent topic in network science as it allows the identification of functional building blocks in complex systems. When such building blocks may be present in graphons is an open question. In this paper, we define a graphon-modularity and demonstrate that it can be maximised to detect communities in graphons. We then investigate specific synthetic graphons and show that they may show a wide range of different community structures. We also reformulate the graphon-modularity maximisation as a continuous optimisation problem and so prove the optimal community structure or lack thereof for some graphons, something that is usually not possible for networks. Furthermore, we demonstrate that estimating a graphon from network data as an intermediate step can improve the detection of communities, in comparison with exclusively maximising the modularity of the network. While the choice of graphon-estimator may strongly influence the accord between the community structure of a network and its estimated graphon, we find that there is a substantial overlap if an appropriate estimator is used. Our study demonstrates that community detection for graphons is possible and may serve as a privacy-preserving way to cluster network data.
Provable Generalization of SGD-trained Neural Networks of Any Width in the Presence of Adversarial Label Noise

We consider a one-hidden-layer leaky ReLU network of arbitrary width trained by stochastic gradient descent following an arbitrary initialization. We prove that stochastic gradient descent (SGD) produces neural networks that have classification accuracy competitive with that of the best halfspace over the distribution for a broad class of distributions that includes log-concave isotropic and hard margin distributions. Equivalently, such networks can generalize when the data distribution is linearly separable but corrupted with adversarial label noise, despite the capacity to overfit. We conduct experiments which suggest that for some distributions our generalization bounds are nearly tight. This is the first result that shows that overparameterized neural networks trained by SGD can generalize when the data is corrupted with adversarial label noise.
RETICOLO software for grating analysis

RETICOLO implements the rigorous coupled wave analysis (RCWA) for 1D (classical and conical diffraction) and 2D crossed gratings. It operates under a MATLAB environment and incorporates an efficient and accurate toolbox for computing Bloch modes and visualizing the electromagnetic field in the grating region. As a spin-off, the Version V9 launched in 2021 includes a toolbox for the analysis of stacks of arbitrarily anisotropic multilayered thin-films.
UnitedQA: A Hybrid Approach for Open Domain Question Answering

To date, most of recent work under the retrieval-reader framework for open-domain QA focuses on either extractive or generative reader exclusively. In this paper, we study a hybrid approach for leveraging the strengths of both models. We apply novel techniques to enhance both extractive and generative readers built upon recent pretrained neural language models, and find that proper training methods can provide large improvement over previous state-of-the-art models. We demonstrate that a simple hybrid approach by combining answers from both readers can efficiently take advantages of extractive and generative answer inference strategies and outperforms single models as well as homogeneous ensembles. Our approach outperforms previous state-of-the-art models by 3.3 and 2.7 points in exact match on NaturalQuestions and TriviaQA respectively.
An Artificial Intelligence System for Combined Fruit Detection and Georeferencing, Using RTK-Based Perspective Projection in Drone Imagery

This work presents an Artificial Intelligence (AI) system, based on the Faster Region-Based Convolution Neural Network (Faster R-CNN) framework, which detects and counts apples from oblique, aerial drone imagery of giant commercial orchards. To reduce computational cost, a novel precursory stage to the network is designed to preprocess raw imagery into cropped images of individual trees. Unique geospatial identifiers are allocated to these using the perspective projection model. This employs Real-Time Kinematic (RTK) data, Digital Terrain and Surface Models (DTM and DSM), as well as internal and external camera parameters. The bulk of experiments however focus on tuning hyperparameters in the detection network itself. Apples which are on trees and apples which are on the ground are treated as separate classes. A mean Average Precision (mAP) metric, calibrated by the size of the two classes, is devised to mitigate spurious results. Anchor box design is of key interest due to the scale of the apples. As such, a k-means clustering approach, never before seen in literature for Faster R-CNN, resulted in the most significant improvements to calibrated mAP. Other experiments showed that the maximum number of box proposals should be 225; the initial learning rate of 0.001 is best applied to the adaptive RMS Prop optimiser; and ResNet 101 is the ideal base feature extractor when considering mAP and, to a lesser extent, inference time. The amalgamation of the optimal hyperparameters leads to a model with a calibrated mAP of 0.7627.
CHEOPS observations of the HD 108236 planetary system: a fifth planet, improved ephemerides, and planetary radii

Context. The detection of a super-Earth and three mini-Neptunes transiting the bright (V = 9.2 mag) star HD 108236 (also known as TOI-1233) was recently reported on the basis of TESS and ground-based light curves. Aims. We perform a first characterisation of the HD 108236 planetary system through high-precision CHEOPS photometry and improve the transit ephemerides and system parameters. Methods. We characterise the host star through spectroscopic analysis and derive the radius with the infrared flux method. We constrain the stellar mass and age by combining the results obtained from two sets of stellar evolutionary tracks. We analyse the available TESS light curves and one CHEOPS transit light curve for each known planet in the system. Results. We find that HD 108236 is a Sun-like star with R⋆ = 0.877 ± 0.008 R⊙, M⋆ = 0.869−0.048+0.050 M⊙, and an age of 6.7−5.1+4.0 Gyr. We report the serendipitous detection of an additional planet, HD 108236 f, in one of the CHEOPS light curves. For this planet, the combined analysis of the TESS and CHEOPS light curves leads to a tentative orbital period of about 29.5 days. From the light curve analysis, we obtain radii of 1.615 ± 0.051, 2.071 ± 0.052, 2.539−0.065+0.062, 3.083 ± 0.052, and 2.017−0.057+0.052 R⊕ for planets HD 108236 b to HD 108236 f, respectively. These values are in agreement with previous TESS-based estimates, but with an improved precision of about a factor of two. We perform a stability analysis of the system, concluding that the planetary orbits most likely have eccentricities smaller than 0.1. We also employ a planetary atmospheric evolution framework to constrain the masses of the five planets, concluding that HD 108236 b and HD 108236 c should have an Earth-like density, while the outer planets should host a low mean molecular weight envelope. Conclusions. The detection of the fifth planet makes HD 108236 the third system brighter than V = 10 mag to host more than four transiting planets. The longer time span enables us to significantly improve the orbital ephemerides such that the uncertainty on the transit times will be of the order of minutes for the years to come. A comparison of the results obtained from the TESS and CHEOPS light curves indicates that for a V ~ 9 mag solar-like star and a transit signal of ~500 ppm, one CHEOPS transit light curve ensures the same level of photometric precision as eight TESS transits combined, although this conclusion depends on the length and position of the gaps in the light curve.
Curing inflationary degeneracies using reheating predictions and relic gravitational waves

It is well known that the inflationary scenario often displays different sets of degeneracies in its predictions for CMB observables. These degeneracies usually arise either because multiple inflationary models predict similar values for the scalar spectral index n S and the tensor-to-scalar ratio r, or because within the same model, the values of {n S , r} are insensitive to some of the model parameters, making it difficult for CMB observations alone to constitute a unique probe of inflationary cosmology. We demonstrate that by taking into account constraints on the post-inflationary reheating parameters such as the duration of reheating N re, its temperature T re and especially its equation of state (EOS), w re, it is possible to break this degeneracy in certain classes of inflationary models where identical values of {n S , r} can correspond to different reheating w re. In particular, we show how reheating constraints can break inflationary degeneracies in the T-model and the E-model α-attractors. Non-canonical inflation is also studied. The relic gravitational wave (GW) spectrum provides us with another tool to break inflationary degeneracies. This is because the GW spectrum is sensitive to the post-inflationary EOS of the universe. Indeed a stiff EOS during reheating (w re > 1/3) gives rise to a small scale blue tilt in the spectral index n GW = d log ΩGW/d log k > 0, while a soft EOS (w re < 1/3) results in a red tilt. Relic GWs therefore provide us with valuable information about the post-inflationary epoch, and their spectrum can be used to cure inflationary degeneracies in {n S , r}.
BanglaBERT: Combating Embedding Barrier for Low-Resource Language Understanding

Pre-training language models on large volume of data with self-supervised objectives has become a standard practice in natural language processing. However, most such state-of-the-art models are available in only English and other resource-rich languages. Even in multilingual models, which are trained on hundreds of languages, low-resource ones still remain underrepresented. Bangla, the seventh most widely spoken language in the world, is still low in terms of resources. Few down-stream task datasets for language understanding in Bangla are publicly available, and there is a clear shortage of good quality data for pre-training. In this work, we build a Bangla natu-ral language understanding model pre-trained on 18.6 GB data we crawled from top Bangla sites on the internet. We introduce a new downstream task dataset and benchmark on four tasks on sentence classiﬁcation, document classiﬁcation, natural language understanding, and sequence tagging. Our model outperforms multilingual baselines and previous state-of-the-art results by 1-6%. In the process, we identify a major shortcoming of multilingual models that hurt performance for low-resource languages that don’t share writing scripts with any high resource one, which we name the ‘Embedding Barrier’. We perform extensive experiments to study this barrier. We release all our datasets and pre-trained models to aid future NLP research on Bangla and other low-resource languages. Our code and data are available at github.com/csebuetnlp/ banglabert .
Identification of Odor-active Components of Agarwood Essential Oils from Thailand by Solid Phase Microextraction-GC/MS and GC-O

Abstract Volatile components from the essential oils of Aquilaria malaccensis, A. subintegra and A. crassna were investigated by gas chromatography (GC), gas chromatography-mass spectrometry (GC/MS), and gas chromatography-olfactometry (GC-O). A total of 31 volatile constituents were identified from the three agarwood oils. A subset of 18 out of the 31 components was identified from the essential oil of A. malaccensis with the major components being isoamyl dodecanoate, guaia-1(10),11-dien-15-ol, karanone, cyclocolorenone and jinkoheremol. Aquilaria subintegra oil yielded 28 identified compounds with the most abundant components being isoamyl dodecanoate, kusunol, jinkoh-eremol, epoxybulnesene and β-agarofuran, while 30 volatile compounds from A. crassna were identified, with isoamyl dodecanoate, β-agarofuran, kusunol, dehydrojinkoh-eremol and 9,11-eremophiladien-8-one as the main constituents. The aroma-active compounds of the three samples were analyzed by HS-SPME-GC-O. A total of 74 odor-active components were characterized by using the GC-O technique. The major components responsible for the aroma included β-agarofuran, 4-phenyl-2-butanone, furfural and benzaldehyde, while the minor aroma notes were attributed to (E)-α-bergamotene, α-humulene, α-bulnesene, α-agarofuran, nor-ketoagarofuran, epoxybulnesene, agarospirol, jinkoh-eremol, kusunol, acorenone B, selina-3,11-dien-14-al and 9,11-eremophiladien-8-one, considered to be the most important aroma impact compounds for the characteristic aroma of agarwood essential oils
New Directions in Cloud Programming

Nearly twenty years after the launch of AWS, it remains difficult for most developers to harness the enormous potential of the cloud. In this paper we lay out an agenda for a new generation of cloud programming research aimed at bringing research ideas to programmers in an evolutionary fashion. Key to our approach is a separation of distributed programs into a PACT of four facets: Program semantics, Availablity, Consistency and Targets of optimization. We propose to migrate developers gradually to PACT programming by lifting familiar code into our more declarative level of abstraction. We then propose a multi-stage compiler that emits human-readable code at each stage that can be hand-tuned by developers seeking more control. Our agenda raises numerous research challenges across multiple areas including language design, query optimization, transactions, distributed consistency, compilers and program synthesis.