Miracle-dz/UniFAR-FTUs huggingface dataset:
Relative Pose Estimation for Multi-Camera Systems from Point Correspondences with Scale Ratio

The use of multi-camera systems is becoming more common in self-driving cars, micro aerial vehicles or augmented reality headsets. In order to perform 3D geometric tasks, the accuracy and efficiency of relative pose estimation algorithms are very important for the multi-camera systems, and is catching significant research attention these days. The point coordinates of point correspondences (PCs) obtained from feature matching strategies have been widely used for relative pose estimation. This paper exploits known scale ratios besides the point coordinates, which are also intrinsically provided by scale invariant feature detectors (e.g., SIFT). Two-view geometry of scale ratio associated with the extracted features is derived for multi-camera systems. Thanks to the constraints provided by the scale ratio across two views, the number of PCs needed for relative pose estimation is reduced from 6 to 3. Requiring fewer PCs makes RANSAC-like randomized robust estimation significantly faster. For different point correspondence layouts, four minimal solvers are proposed for typical two-camera rigs. Extensive experiments demonstrate that our solvers have better accuracy than the state-of-the-art ones and outperform them in terms of processing time.
STELLAR PARAMETERS AND METALLICITIES OF STARS HOSTING JOVIAN AND NEPTUNIAN MASS PLANETS: A POSSIBLE DEPENDENCE OF PLANETARY MASS ON METALLICITY

The metal content of planet-hosting stars is an important ingredient that may affect the formation and evolution of planetary systems. Accurate stellar abundances require the determinations of reliable physical parameters, namely, the effective temperature, surface gravity, microturbulent velocity, and metallicity. This work presents the homogeneous derivation of such parameters for a large sample of stars hosting planets (N = 117), as well as a control sample of disk stars not known to harbor giant, closely orbiting planets (N = 145). Stellar parameters and iron abundances are derived from an automated analysis technique developed for this work. As previously found in the literature, the results in this study indicate that the metallicity distribution of planet-hosting stars is more metal rich by ∼0.15 dex when compared to the control sample stars. A segregation of the sample according to planet mass indicates that the metallicity distribution of stars hosting only Neptunian-mass planets (with no Jovian-mass planets) tends to be more metal poor in comparison with that obtained for stars hosting a closely orbiting Jovian planet. The significance of this difference in metallicity arises from a homogeneous analysis of samples of FGK dwarfs which do not include the cooler and more problematic M dwarfs. This result would indicate that there is a possible link between planet mass and metallicity such that metallicity plays a role in setting the mass of the most massive planet. Further confirmation, however, must await larger samples.
The H2O Southern Galactic Plane Survey (HOPS) – I. Techniques and H2O maser data

We present first results of the H2O Southern Galactic Plane Survey (HOPS), using the Mopra Radio Telescope with a broad-band backend and a beam size of about 2 arcmin. We have observed 100 deg2 of the southern Galactic plane at 12 mm (19.5-27.5 GHz), including spectral line emission from H2O masers, multiple metastable transitions of ammonia, cyanoacetylene, methanol and radio recombination lines. In this paper, we report on the characteristics of the survey and H2O maser emission. We find 540 H2O masers, of which 334 are new detections. The strongest maser is 3933 Jy and the weakest is 0.7 Jy, with 62 masers over 100 Jy. In 14 maser sites, the spread in the velocity of the H2O maser emission exceeds 100 km s-1. In one region, the H2O maser velocities are separated by 351.3 km s-1. The rms noise levels are typically between 1 and 2 Jy, with 95 per cent of the survey under 2 Jy. We estimate completeness limits of 98 per cent at around 8.4 Jy and 50 per cent at around 5.5 Jy. We estimate that there are between 800 and 1500 H2O masers in the Galaxy that are detectable in a survey with similar completeness limits to HOPS. We report possible masers in NH3 (11,9) and (8,6) emission towards G19.61-0.23 and in the NH3 (3,3) line towards G23.33-0.30.
Approximation algorithms for geometric problems

This chapter surveys approximation algorithms for hard geometric problems. The problems we consider typically take inputs that are point sets or polytopes in twoor three-dimensional space, and seek optimal constructions, (which may be trees, paths, or polytopes). We limit attention to problems for which no polynomial-time exact algorithms are known, and concentrate on bounds for worst-case approximation ratios, especially bounds that depend intrinsically on geometry. We illustrate our intentions with two well-known problems. Given a finite set of points S in the plane, the Euclidean traveling salesman problem asks for the shortest tour of S. Christofides’ algorithm achieves approximation ratio 2 for this problem, meaning that it always computes a tour of length at most three-halves the length of the optimal tour. This bound depends only on the triangle inequality, so Christofides’ algorithm works equally well in any metric space, even the finite metric space induced by the shortest-path distance on a network (edge-weighted graph). The Steiner tree problem asks for the shortest tree that includes S in its vertex set. Approximation ratios for this problem depend quite intimately on geometry. For
The energy and lifetime of resonant states with real basis sets

Using a probabilistic interpretation of resonant states and square-integrable real basis set expansion techniques, we propose a formula useful to calculate the lifetime of a resonance. Our approach does not require an estimation of the density of states. The method is illustrated with calculations of s- and p-resonant state energies and lifetimes.
Point Cloud Completion via Multi-Scale Edge Convolution and Attention

Point cloud completion aims to recover a complete shape of a 3D object from its partial observation. Existing methods usually predict complete shapes from global representations, consequently, local geometric details may be ignored. Furthermore, they tend to overlook relations among different local regions, which are valuable during shape inference. To solve these problems, we propose a novel point cloud completion network based on multi-scale edge convolution and attention mechanism, named MEAPCN. We represent a point cloud as a set of embedded points, each of which contains geometric information of local patches around it. Firstly, we devise an encoder to extract multi-scale local features of the input point cloud and produce partial embedded points. Then, we generate coarse complete embedded points to represent the overall shape. In order to enrich features of complete embedded points, attention mechanism is utilized to selectively aggregate local informative features of partial ones. Lastly, we recover a fine-grained point cloud with highly detailed geometries using folding-based strategy. To better reflect real-world occlusion scenarios, we contribute a more challenging dataset, which consists of view-occluded partial point clouds. Experimental results on various benchmarks demonstrate that our method achieves a superior completion performance with much smaller model size and much lower computation cost.
A Numerical DEs Perspective on Unfolded Linearized ADMM Networks for Inverse Problems

Many research works show that the continuous-time Differential Equations (DEs) allow for a better understanding of traditional Alternating Direction Multiplier Methods (ADMMs). And many unfolded algorithms directly inherit the traditional iterations to build deep networks. Although they obtain a faster convergence rate and superior practical performance, there is a lack of an appropriate explanation of the unfolded network architectures. Thus, we attempt to explore the connection between the existing unfolded Linearized ADMM (LADMM) and numerical DEs, and propose efficient unfolded network design schemes. First, we present an unfolded Euler LADMM scheme as a by-product, which originates from the Euler method for solving first-order DEs. Then inspired by the trapezoid method in numerical DEs, we design a new more effective network scheme, called unfolded Trapezoid LADMM scheme. Moreover, we analyze that the Trapezoid LADMM scheme has higher precision than the Euler LADMM scheme. To the best of our knowledge, this is the first work to explore the connection between unfolded ADMMs and numerical DEs with theoretical guarantees. Finally, we instantiate our Euler LADMM and Trapezoid LADMM schemes into ELADMM and TLADMM with the proximal operators, and ELADMM-Net and TLADMM-Net with convolutional neural networks. And extensive experiments show that our algorithms are competitive with state-of-the-art methods.
GAT-DASeq2Seq: A Water Quality Prediction Model Based on Attention Mechanism and Fusion Spatio-temporal Features

Aiming at the problem that the existing models are difficult to accurately extract the global spatiotemporal features, a surface water quality prediction model GAT-DASeq2Seq based on the attention mechanism and fusion spatio-temporal features is proposed. Spatial features between water quality monitoring sites were extracted with GAT, and then the key features and key time points of historical water quality time series were extracted using the DASeq2Seq model that introduced a two-stage attention mechanism. The experiments were carried out with 15 months of historical data from 6 water quality monitoring stations in the Huangshui River and its surrounding areas in Xining City, Qinghai Province, and the comparison model of the ablation experiment was selected for the comparison experiment. The experimental results show that the proposed model can effectively Improve the accuracy of multi-step prediction of surface water quality.
Non-Linear Amplification of a Magnetic Field Driven by Cosmic Ray Streaming

One dimensional numerical results of the non-linear interaction between cosmic rays and a magnetic field are presented. These show that cosmic ray streaming drives large amplitude Alfvénic waves. The cosmic ray streaming energy is very efficiently transfered to the perturbed magnetic field of the Alfvén waves. Thus a magnetic field of interstellar values, assumed in models of supernova remnant blast wave acceleration, would not be appropriate in the region of the shock. The increased magnetic field reduces the acceleration time and so increases the maximum cosmic ray energy, which may provide a simple and elegant resolution to the highest energy galactic cosmic ray problem were the cosmic rays themselves provide the fields necessary for their acceleration.
Black-body radiation for twist-deformed space-time

In this article we formally investigate the impact of twisted space-time on black-body radiation phenomena, i.e. we derive the $\theta$-deformed Planck distribution function as well as we perform its numerical integration to the $\theta$-deformed total radiation energy. In such a way we indicate that the space-time noncommutativity very strongly damps the black-body radiation process. Besides we provide for small parameter $\theta$ the twisted counterparts of Rayleigh-Jeans and Wien distributions respectively.
Generating Smooth and Facial-Details-Enhanced Talking Head Video: A Perspective of Pre and Post Processes

Talking head video generation has received increasing attention recently. So far the quality (especially the facial details) of the videos output from state-of-the-art deep learning methods is limited by either the quality of training data or the performance of generators, and needs to be further improved. In this paper, we propose a data pre- and post- processing strategy based on a key observation: generating talking head video from multi-modal input is a challenging problem and generating smooth video with fine facial details makes the problem even harder. Then we propose to decompose the problem solution into a main deep model, a pre- and a post- processing. The main deep model generates a reasonably good talking face video, with the aid of a pre-process, which also contributes to a post-process for restoring smooth and fine facial details in the final video. In particular, our main deep model reconstructs a 3D face from an input reference frame, and then uses an AudioNet to generate a sequence of facial expression coefficients with an input audio clip. To ensure final facial details in the generated video, we sample the original texture from the reference frame in the pre-process with the aid of reconstructed 3D face and a predefined UV map. Accordingly, in the post-process, we smooth the expression coefficients of adjacent frames to alleviate jitters and apply a pretrained face restoration module to recover the fine facial details. Experimental results and ablation study show the advantage of our proposed method.
Manipulating Foley Footsteps and Character Realism to Influence Audience Perceptions of a 3D Animated Walk Cycle

Foley artistry is an essential part of the audio post-production process for film, television, games, and animation. By extension, it is as crucial in emergent media such as virtual, mixed, and augmented reality. Footsteps are a core activity that a Foley artist must undertake and convey information about the characters and environment presented on-screen. This study sought to identify if characteristics of age, gender, weight, health, and confidence could be conveyed, using sounds created by a professional Foley artist, in three different 3D humanoid models, following a single walk cycle. An experiment was conducted with human participants (n=100) and found that Foley manipulations could convey all the intended characteristics with varying degrees of contextual success. It was shown that the abstract models were capable of communicating characteristics of age, gender, and weight. The findings are relevant to researchers and practitioners in linear and interactive media and demonstrate mechanisms by which Foley can contribute useful information and concepts about on-screen characters.
CurML: A Curriculum Machine Learning Library

Curriculum learning (CL) is a machine learning paradigm gradually learning from easy to hard, which is inspired by human curricula. As an easy-to-use and general training strategy, CL has been widely applied to various multimedia tasks covering images, texts, audios, videos, etc. The effectiveness of CL has recently facilitated an increasing number of new CL algorithms. However, there has been no open-source library for curriculum learning, making it hard to reproduce, evaluate and compare the numerous CL algorithms on fair benchmarks and settings. To ease and promote future research on CL, we develop CurML, the first Curriculum Machine L earning library to integrate existing CL algorithms into a unified framework. It is convenient to use and flexible to customize by calling the provided five APIs, which are designed for easily plugging into a general training process and conducting the data-oriented, model-oriented and loss-oriented curricula. Furthermore, we present empirical results obtained by CurML to demonstrate the advantages of our library. The code is available online at https://github.com/THUMNLab/CurML.
X-RAY CHARACTERISTICS OF NGC 3516: A VIEW THROUGH THE COMPLEX ABSORBER

We consider new Suzaku data for NGC 3516 taken during 2009 along with other recent X-ray observations of the source. The cumulative characteristics of NGC 3516 cannot be explained without invoking changes in the line-of-sight absorption. Contrary to many other well-studied Seyfert galaxies, NGC 3516 does not show a positive lag of hard X-ray photons relative to soft photons over the timescales sampled. In the context of reverberation models for the X-ray lags, the lack of such a signal in NGC 3516 is consistent with flux variations being dominated by absorption changes. The lack of any reverberation signal in such a highly variable source disfavors intrinsic continuum variability in this case. Instead, the colorless flux variations observed at high flux states for NGC 3516 are suggested to be a consequence of Compton-thick clumps of gas crossing the line of sight.
AEDNet: Asynchronous Event Denoising with Spatial-Temporal Correlation among Irregular Data

Dynamic Vision Sensor (DVS) is a compelling neuromorphic camera compared to conventional camera, but it suffers from fiercer noise. Due to the nature of irregular format and asynchronous readout, DVS data is always transformed into a regular tensor (e.g., 3D voxel or image) for deep learning method, which corrupts its own asynchronous properties. To maintain asynchronous, we establish an innovative asynchronous event denoise neural network, named AEDNet, which directly consumes the correlation of the irregular signal in spatial-temporal range without destroying its original structural property. Based on the property of continuation in temporal domain and discreteness in spatial domain, we decompose the DVS signal into two parts, i.e., temporal correlation and spatial affinity, and separately process these two parts. Our spatial feature embedding unit is a unique feature extraction module that extracts feature from event-level, which perfectly maintains its spatial-temporal correlation. To test effectiveness, we build a novel dataset named DVSCLEAN containing both simulated and real-world data. The experimental results of AEDNet achieve SOTA.
Factors Aﬀecting Consumer Buying Behavior in E-Commerce Business during Outbreak of Covid-19: A Case Study on Top E-Commerce Websites

This research was conducted to investigate the factors that have affected the consumer buying behavior in ecommerce business, especially during COVID-19. This research was conducted via quantitative analysis, and 200 participants were recruited for analysis. The statistical tests that were conducted in this research included demogr aphics via frequency analysis, Cronbach’s Alpha test for the questionnaire reliability, correlation analysis to test the strength of the relationship in between variables, and regression analysis for finding the impact of consumer behavior on the dependent variables of social media campaigns, television commercials, e-paper advertisements, and word of mouth marketing. In this context, the research found that social media campaigns and television commercials had no significant impact on the consumer buying behavior, whereas e-paper advertisements and word of mouth marketing had significant impact on the buying behavior of consumers during COVID-19. Based on the findings of this research, it was recommended that e-commerce businesses must upload the articles on social media regarding the product or services that are offered by the business so that the customer attains a complete understanding of the products or services, which will attract the customers and will influence the purchasing behavior of the customers positively.
Transcriptome and functional analyses reveal ERF053 from Medicago falcata as key regulator in drought resistances

Medicago falcata L. is an important legume forage grass with strong drought resistant, which could be utilized as an important gene pool in molecular breed of forage grass. In this study, M. falcata seedlings were treated with 400 mM mannitol to simulate drought stress, and the morphological and physiological changes were investigated, as well as the transcriptome changes of M. falcata seedlings at different treatment time points (0 h, 2 h, 6 h, 12 h, 24 h, 36 h and 48 h). Transcriptome analyses revealed four modules were closely related with drought response in M. falcata by WGCNA analysis, and four ERF transcription factor genes related with drought stress were identified (MfERF053, MfERF9, MfERF034 and MfRAP2.1). Among them, MfERF053 was highly expressed in roots, and MfERF053 protein showed transcriptional activation activity by transient expression in tobacco leaves. Overexpression of MfERF053 in Arabidopsis improved root growth, number of lateral roots and fresh weight under drought, salt stress and exogenous ABA treatments. Transgenic Arabidopsis over-expressing MfERF053 gene grew significantly better than the wild type under both drought stress and salt stress when grown in soil. Taken together, our strategy with transcriptome combined WGCNA analyses identified key transcription factor genes from M. falcata, and the selected MfERF053 gene was verified to be able to enhance drought and salt resistance when over-expressed in Arabidopsis.
Effect of guanidine acetic acid on meat quality, muscle amino acids, and fatty acids in Tibetan pigs

This study investigated the effects of guanidine acetic acid (GAA) supplementation on growth performance, carcass traits, and meat quality in Tibetan pigs. A total of 18 male Tibetan pigs (21.35 ± 0.99 kg) were randomly assigned to the control (basal diet) and GAA (basal diet + 800 mg/kg GAA) groups for 125 days. Growth performance, carcass traits, and meat quality in pigs, and the chemical composition of Longissimus thoracis (LT) were not altered by GAA. In LT, compared to the control group, dietary GAA increased the superoxide dismutase activity, transcripts of stearoyl CoA desaturase (SCD) and fatty acid synthase (FAS), and contents of glutamate, glutamine, C24:0, C20:3n-6, C20:4n-6, and polyunsaturated fatty acids (P < 0.05), but it decreased the malondialdehyde content (P < 0.001). In back fat, dietary GAA reduced the transcript of peroxisome proliferator-activated receptor γ (PPARγ) and the contents of C10:0, C12:0, C14:0, and C16:0 (P < 0.05), whereas it increased the contents of C22:0, C20:1, C22:1, C24:1, C20:2, C20:3n-3, and C22:2 (P < 0.05). These findings will provide a basis for high-quality Tibetan pork production.
Editorial: New challenges with the management of rheumatic heart disease

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