Miracle-dz/UniFAR-FTUs huggingface dataset:
HDR Video Reconstruction with Tri-Exposure Quad-Bayer Sensors

We propose a novel high dynamic range (HDR) video reconstruction method with new tri-exposure quad-bayer sensors. Thanks to the larger number of exposure sets and their spatially uniform deployment over a frame, they are more robust to noise and spatial artifacts than previous spatially varying exposure (SVE) HDR video methods. Nonetheless, the motion blur from longer exposures, the noise from short exposures, and inherent spatial artifacts of the SVE methods remain huge obstacles. Additionally, temporal coherence must be taken into account for the stability of video reconstruction. To tackle these challenges, we introduce a novel network architecture that divides-and-conquers these problems. In order to better adapt the network to the large dynamic range, we also propose LDR-reconstruction loss that takes equal contributions from both the highlighted and the shaded pixels of HDR frames. Through a series of comparisons and ablation studies, we show that the tri-exposure quad-bayer with our solution is more optimal to capture than previous reconstruction methods, particularly for the scenes with larger dynamic range and objects with motion.
Complex Structure of the Eastern Lobe of the Pictor A Radio Galaxy: Spectral Analysis and X-Ray/Radio Correlations

Here we present detailed analysis of the distinct X-ray emission features present within the eastern radio lobe of the Pictor A galaxy, around the jet termination region, utilizing the data obtained from the Chandra X-ray Observatory. Various emission features have been selected for the study based on their enhanced X-ray surface brightness, including five sources that appear pointlike, as well as three extended regions, one characterized by a filamentary morphology. For those, we perform a basic spectral analysis within the 0.5–7 keV range. We also investigate various correlations between the X-ray emission features and the nonthermal radio emission, utilizing the high-resolution radio maps from the Very Large Array at gigahertz frequencies. The main novel findings following from our analysis concern the newly recognized bright X-ray filament located upstream of the jet termination region, extending for at least 30 kpc (projected), and inclined with respect to the jet axis. For this feature, we observe a clear anticorrelation between the X-ray surface brightness and the polarized radio intensity, as well as a decrease in the radio rotation measure with respect to the surroundings. We speculate on the nature of the filament, in particular addressing a possibility that it is related to the presence of a hot X-ray-emitting thermal gas, only partly mixed with the nonthermal radio/X-ray-emitting electrons within the lobe, combined with the reversals in the lobe’s net magnetic field.
Inclusion compounds with mixed guests: controlled stoichiometries and kinetics of enclathration

The host compound trans-9,10-dihydroxy-9,10-bis(p-tert-butylphenyl)-9,10-dihydroanthracene forms inclusion compounds with DMF, DMSO and acetone of the type H·nG1·(4 − n)G2, with n varying integrally from 0 to 4. The structures of these compounds have been elucidated. The kinetics of enclathration with acetone vapour displays anti-Arrhenius behaviour and the desorption process has a low activation energy of 48.4 kJ mol−1.
ANITA: An Optimal Loopless Accelerated Variance-Reduced Gradient Method

In this paper, we propose a novel accelerated gradient method called ANITA for solving the fundamental ﬁnite-sum optimization problems. Concretely, we consider both general convex and strongly convex settings: i) For general convex ﬁnite-sum problems, ANITA improves previous state-of-the-art result given by Varag (Lan et al., 2019). In particular, for large-scale problems or the convergence error is not very small, i.e., n ≥ 1 (cid:15) 2 , ANITA obtains the ﬁrst optimal result O ( n ), matching the lower bound Ω( n ) provided by Woodworth and Srebro (2016), while previous results are O ( n log 1 (cid:15) ) of Varag (Lan et al., 2019) and O ( n √ (cid:15) ) of Katyusha (Allen-Zhu, 2017). ii) For strongly convex ﬁnite-sum problems, we also show that ANITA can achieve the optimal convergence rate O (cid:0) ( n + (cid:113) nLµ ) log 1 (cid:15) (cid:1) matching the lower bound Ω (cid:0) ( n + (cid:113) nLµ ) log 1 (cid:15) (cid:1) provided by Lan and Zhou (2015). Besides, ANITA enjoys a simpler loopless algorithmic structure unlike previous accelerated algorithms such as Varag (Lan et al., 2019) and Katyusha (Allen-Zhu, 2017) where they use double-loop structures. Moreover, we provide a novel dynamic multi-stage convergence analysis , which is the key technical part for improving previous results to the optimal rates. We believe that our new theoretical rates and novel convergence analysis for the fundamental ﬁnite-sum problem will directly lead to key improvements for many other related problems, such as distributed/federated/decentralized optimization problems (e.g., Li and Richt´arik, 2021). Finally, the numerical experiments show that ANITA converges faster than the previous state-of-the-art Varag (Lan et al., 2019), validating our theoretical results and conﬁrming the practical superiority of ANITA .
Revisiting the mass- and radius–luminosity relations for FGK main-sequence stars

Context. Scaling relations are very useful tools for estimating unknown stellar quantities. Within this framework, eclipsing binaries are ideal for this goal because their mass and radius are known with a very good level of accuracy, leading to improved constraints on the models. Aims. We aim to provide empirical relations for the mass and radius as function of luminosity, metallicity, and age. We investigate, in particular, the impact of metallicity and age on those relations. Methods. We used a multi-dimensional fit approach based on the data from DEBCat, an updated catalogue of eclipsing binary observations such as mass, radius, luminosity, effective temperature, gravity, and metallicity. We used the PARAM web interface for the Bayesian estimation of stellar parameters, along with the stellar evolutionary code MESA to estimate the binary age, assuming a coeval hypothesis for both members. Results. We derived the mass and radius–luminosity–metallicity–age relations using 56 stars, with metallicity and mass in the range −0.34 < [Fe/H] < 0.27 and 0.66 < M/M⊙ < 1.8. With that, the observed mass and radius are reproduced with an accuracy of 3.5% and 5.9%, respectively, which is consistent with the other results in literature. Conclusions. We conclude that including the age in such relations increases the quality of the fit, particularly in terms of the mass, as compared to the radius. On the other hand, as otherss authors have noted, we observed an higher dispersion on the mass relation than in that of the radius. We propose that this is due to a stellar age effect.
Force sensing in robot-assisted keyhole endoscopy: A systematic survey

Instrument–tissue interaction forces in minimally invasive surgery (MIS) provide valuable information that can be used to provide haptic perception, monitor tissue trauma, develop training guidelines, and evaluate the skill level of novice and expert surgeons. Force and tactile sensing is lost in many robot-assisted surgery (RAS) systems. Therefore, many researchers have focused on recovering this information through sensing systems and estimation algorithms. This article provides a comprehensive systematic review of the current force sensing research aimed at RAS and, more generally, keyhole endoscopy, in which instruments enter the body through small incisions. Articles published between January 2011 and May 2020 are considered, following the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines. The literature search resulted in 110 papers on different force estimation algorithms and sensing technologies, sensor design specifications, and fabrication techniques.
Reinforcement Learning based on Scenario-tree MPC for ASVs

In this paper, we present the use of Reinforcement Learning (RL) based on Robust Model Predictive Control (RMPC) for the control of an Autonomous Surface Vehicle (ASV). The RL-MPC strategy is utilized for obstacle avoidance and target (set-point) tracking. A scenario-tree robust MPC is used to handle potential failures of the ship thrusters. Besides, the wind and ocean current are considered as unknown stochastic disturbances in the real system, which are handled via constraints tightening. The tightening and other cost parameters are adjusted by RL, using a Q-Iearning technique. An economic cost is considered, minimizing the time and energy required to achieve the ship missions. The method is illustrated in simulation on a nonlinear 3-DOF model of a scaled version of the Cybership II.
Note on the Number of Balanced Independent Sets in the Hamming Cube

Let $Q_d$ be the $d$-dimensional Hamming cube and $N=|V(Q_d)|=2^d$. An independent set $I$ in $Q_d$ is called balanced if $I$ contains the same number of even and odd vertices. We show that the logarithm of the number of balanced independent sets in $Q_d$ is \[(1-\Theta(1/\sqrt d))N/2. \] The key ingredient of the proof is an improved version of "Sapozhenko's graph container lemma".
Asymptotic solution to convolution integral equations on large and small intervals

We consider convolution integral equations on a finite interval with a real-valued kernel of even parity, a problem equivalent to finding a Wiener–Hopf factorization of a notoriously difficult class of 2 × 2 matrices. The kernel function is assumed to be sufficiently smooth and decaying for large values of the argument. Without loss of generality, we focus on a homogeneous equation and we propose methods to construct explicit asymptotic solutions when the interval size is large and small. The large interval method is based on a reduction of the original equation to an integro-differential equation on a half-line that can be asymptotically solved in a closed form. This provides an alternative to other asymptotic techniques that rely on fast (typically exponential) decay of the kernel function at infinity, which is not assumed here. We also consider the problem on a small interval and show that finding its asymptotic solution can be reduced to solving an ODE. In particular, approximate solutions could be constructed in terms of readily available special functions (prolate spheroidal harmonics). Numerical illustrations of the obtained results are provided and further extensions of both methods are discussed.
Thermal degradation of the polyimide synthesized from 4,4′‐(hexafluoroisopropylidene) diphthalic dianhydride and 4,4′‐diaminodiphenylmethane

The thermal degradation of polyimide PIF2 synthesized from 4,4′-(hexafluoroisopropylidene) diphthalic dianhydride and 4,4′-diaminodiphenylmethane was systematically and thoroughly investigated by high-resolution pyrolysis gas chromatography/mass spectrometry, thermogravimetric analysis, and Fourier transform infrared spectroscopy. The pyrolyzates of PIF2 were dependent on the pyrolysis temperature. Two weight-loss peaks were observed in the derivative thermogravimetric analysis of PIF2 and the first peak was related to the fluorine-containing segment (hexafluoroisopropylidene). The overall activation energy and preexponential factor were calculated through dynamic TGA with Ozawa's method. Based on the results, a thermal degradation mechanism of the PIF2 was proposed. © 2003 Wiley Periodicals, Inc. J Appl Polym Sci 91: 2295–2301, 2004
BlonD: An Automatic Evaluation Metric for Document-level MachineTranslation

Standard automatic metrics (such as BLEU) are problematic for document-level MT evaluation. They can neither distinguish document-level improvements in translation quality from sentence-level ones, nor can they identify the speciﬁc discourse phenomena that caused the translation errors. To address these problems, we propose an automatic metric BlonD 1 for document-level machine translation evaluation. BlonD takes discourse coherence into consideration by calculating the recall and distance of check-pointing phrases and tags, and further provides comprehensive evaluation scores by combining with n-gram. Extensive comparisons between BlonD and existing evaluation metrics are conducted to illustrate their critical distinctions. Experimental results show that BlonD has a much higher document-level sensitivity with respect to previous metrics. Human evaluation also reveals high Pearson R correlation values between BlonD scores and manual quality judgments. 2
Learn-to-Race: A Multimodal Control Environment for Autonomous Racing

Existing research on autonomous driving primarily focuses on urban driving, which is insufficient for characterising the complex driving behaviour underlying high-speed racing. At the same time, existing racing simulation frameworks struggle in capturing realism, with respect to visual rendering, vehicular dynamics, and task objectives, inhibiting the transfer of learning agents to real-world contexts. We introduce a new environment, where agents Learn-to-Race (L2R) in simulated competition-style racing, using multimodal information—from virtual cameras to a comprehensive array of inertial measurement sensors. Our environment, which includes a simulator and an interfacing training framework, accurately models vehicle dynamics and racing conditions. In this paper, we release the Arrival simulator for autonomous racing. Next, we propose the L2R task with challenging metrics, inspired by learning-to-drive challenges, Formula-style racing, and multimodal trajectory prediction for autonomous driving. Additionally, we provide the L2R framework suite, facilitating simulated racing on high-precision models of real-world tracks. Finally, we provide an official L2R task dataset of expert demonstrations, as well as a series of baseline experiments and reference implementations. We make all code available: https://github.com/learn-to-race/l2r.
Development and Validation of a Deep Learning Model for Prediction of Severe Outcomes in Suspected COVID-19 Infection

COVID-19 patient triaging with predictive outcome of the patients upon first present to emergency department (ED) is crucial for improving patient prognosis, as well as better hospital resources management and cross-infection control. We trained a deep feature fusion model to predict patient outcomes, where the model inputs were EHR data including demographic information, co-morbidities, vital signs and laboratory measurements, plus patient's CXR images. The model output was patient outcomes defined as the most insensitive oxygen therapy required. For patients without CXR images, we employed Random Forest method for the prediction. Predictive risk scores for COVID-19 severe outcomes ("CO-RISK" score) were derived from model output and evaluated on the testing dataset, as well as compared to human performance. The study's dataset (the "MGB COVID Cohort") was constructed from all patients presenting to the Mass General Brigham (MGB) healthcare system from March 1st to June 1st, 2020. ED visits with incomplete or erroneous data were excluded. Patients with no test order for COVID or confirmed negative test results were excluded. Patients under the age of 15 were also excluded. Finally, electronic health record (EHR) data from a total of 11060 COVID-19 confirmed or suspected patients were used in this study. Chest X-ray (CXR) images were also collected from each patient if available. Results show that CO-RISK score achieved area under the Curve (AUC) of predicting MV/death (i.e. severe outcomes) in 24 hours of 0.95, and 0.92 in 72 hours on the testing dataset. The model shows superior performance to the commonly used risk scores in ED (CURB-65 and MEWS). Comparing with physician's decisions, CO-RISK score has demonstrated superior performance to human in making ICU/floor decisions.
Exploiting Method Names to Improve Code Summarization: A Deliberation Multi-Task Learning Approach

Code summaries are brief natural language descriptions of source code pieces. The main purpose of code summarization is to assist developers in understanding code and to reduce documentation workload. In this paper, we design a novel multi-task learning (MTL) approach for code summarization through mining the relationship between method code summaries and method names. More specifically, since a method’s name can be considered as a shorter version of its code summary, we first introduce the tasks of generation and informativeness prediction of method names as two auxiliary training objectives for code summarization. A novel two-pass deliberation mechanism is then incorporated into our MTL architecture to generate more consistent intermediate states fed into a summary decoder, especially when informative method names do not exist. To evaluate our deliberation MTL approach, we carried out a large-scale experiment on two existing datasets for Java and Python. The experiment results show that our technique can be easily applied to many state-of-the-art neural models for code summarization and improve their performance. Meanwhile, our approach shows significant superiority when generating summaries for methods with non-informative names.
Multicycle Study on Chemical-Looping Combustion of Simulated Coal Gas with a CaSO4 Oxygen Carrier in a Fluidized Bed Reactor

Chemical-looping combustion (CLC) is a promising technology for the combustion of gas and solid fuel with efficient use of energy and inherent separation of CO2. In this study, the cyclic test of a CaSO4-based oxygen carrier (natural anhydrite) in alternating reducing simulated coal gas and oxidizing conditions was performed at 950 °C in a fluidized bed reactor at atmospheric pressure. A high concentration of CO2 was obtained in the reduction. The H2 and CO conversions and CO2 yield increased initially and final decreased significantly. The release of SO2 and H2S during the cyclic test was found to be responsible for the decrease of reactivity of a CaSO4 oxygen carrier. The oxygen carrier conversion after the reduction reaction decreased gradually in the cyclic test. Through the comparison of mass-based reaction rates as a function of mass conversion at typical cycles, it was also evident that the reactivity of a CaSO4 oxygen carrier increased for the initial cycles but finally decreased after around 15 c.
Model-based 3D Hand Reconstruction via Self-Supervised Learning

Reconstructing a 3D hand from a single-view RGB image is challenging due to various hand configurations and depth ambiguity. To reliably reconstruct a 3D hand from a monocular image, most state-of-the-art methods heavily rely on 3D annotations at the training stage, but obtaining 3D annotations is expensive. To alleviate reliance on labeled training data, we propose S2HAND, a self-supervised 3D hand reconstruction network that can jointly estimate pose, shape, texture, and the camera viewpoint. Specifically, we obtain geometric cues from the input image through easily accessible 2D detected keypoints. To learn an accurate hand reconstruction model from these noisy geometric cues, we utilize the consistency between 2D and 3D representations and propose a set of novel losses to rationalize outputs of the neural network. For the first time, we demonstrate the feasibility of training an accurate 3D hand reconstruction network without relying on manual annotations. Our experiments show that the proposed self-supervised method achieves comparable performance with recent fully-supervised methods. The code is available at https://github.com/TerenceCYJ/S2HAND.
Mode-wise Tensor Decompositions: Multi-dimensional Generalizations of CUR Decompositions

Low rank tensor approximation is a fundamental tool in modern machine learning and data science. In this paper, we study the characterization, perturbation analysis, and an efficient sampling strategy for two primary tensor CUR approximations, namely Chidori and Fiber CUR. We characterize exact tensor CUR decompositions for low multilinear rank tensors. We also present theoretical error bounds of the tensor CUR approximations when (adversarial or Gaussian) noise appears. Moreover, we show that low cost uniform sampling is sufficient for tensor CUR approximations if the tensor has an incoherent structure. Empirical performance evaluations, with both synthetic and real-world datasets, establish the speed advantage of the tensor CUR approximations over other state-of-the-art low multilinear rank tensor approximations.
Nanophase Separated Perfluorinated Ionomers as Sol‐Gel Polymerization Templates for Functional Inorganic Oxide Nanoparticles

Mauritz et al. exploited the polar/nonpolar nanophase‐separated morphologies of Nafion® perfluorosulfonic acid membranes, as well as a chemically modified Nafion® precursor film, as interactive templates capable of directing sol‐gel hydrolysis→condensation polymerizations of inorganic alkoxides and organoalkoxysilanes and the geometrical distribution of inorganic oxide or organically modified silicate nanophases that result upon drying. This paper reviews our studies involving Nafion® in situ sol‐gel reactions of alkoxides of silicon, titanium, aluminum, zirconium, and organoalkoxysilanes and combinations of these monomers. Also discussed are similar studies by other investigators. We discuss spectroscopic, microscopic, x‐ray scattering, thermal, mechanical tensile, dynamic mechanical, dielectric relaxation, and gas permeation tools that were employed to interrogate the structures and properties of these heterogeneous materials over a range of dimensional scales. Methods of tailoring the energetic environment, or polarity, within the cluster domains by insertion of inorganic oxide or organically‐modified silicate nanostructures are presented. Finally, we discuss the potential for these nanocomposite membranes within a number of areas including gas and liquid separations technology, catalysis, and proton exchange membranes for fuel cell technology.
Interpretable, predictive spatio-temporal models via enhanced pairwise directions estimation

This article concerns predictive modeling for spatio-temporal data as well as model interpretation using data information in space and time. We develop a novel approach based on supervised dimension reduction for such data in order to capture nonlinear mean structures without requiring a prespecified parametric model. In addition to prediction as a common interest, this approach emphasizes the exploration of geometric information from the data. The method of Pairwise Directions Estimation (PDE) is implemented in our approach as a data-driven function searching for spatial patterns and temporal trends. The benefit of using geometric information from the method of PDE is highlighted, which aids effectively in exploring data structures. We further enhance PDE, referring to it as PDE+, by incorporating kriging to estimate the random effects not explained in the mean functions. Our proposal can not only increase prediction accuracy but also improve the interpretation for modeling. Two simulation examples are conducted and comparisons are made with several existing methods. The results demonstrate that the proposed PDE+ method is very useful for exploring and interpreting the patterns and trends for spatio-temporal data. Illustrative applications to two real datasets are also presented.