Miracle-dz/UniFAR-FTUs huggingface dataset:
SEARCH FOR HIGH-ENERGY GAMMA-RAY EMISSION FROM TIDAL DISRUPTION EVENTS WITH THE FERMI LARGE AREA TELESCOPE

Massive black holes at galaxy center may tear apart a star when the star passes occasionally within the disruption radius, which is the so-called tidal disruption event (TDE). Most TDEs radiate with thermal emission resulting from the acceleration disk, but three TDEs have been detected in bright nonthermal X-ray emission, which is interpreted as arising from the relativistic jets. A search for high-energy gamma-ray emission from one relativistic TDE (Swift J164449.3+573451) with the Fermi Large Area Telescope (LAT) has yielded nondetection. In this paper, we report the search for high-energy emission from the other two relativistic TDEs (Swift J2058.4+0516 and Swift J1112.2-8238) during the flare period. No significant GeV emission is found, with an upper limit fluence in the LAT energy range being less than 1% of that in X-rays. Compared with gamma-ray bursts and blazars, these TDEs have the lowest flux ratio between GeV emission and X-ray emission. The nondetection of high-energy emission from relativistic TDEs could be due to the fact that the high-energy emission is absorbed by soft photons in the source. Based on this hypothesis, upper limits on the bulk Lorentz factors, , are then obtained for the jets in these TDEs. We also search for high-energy gamma-ray emission from the nearest TDE discovered to date, ASASSN-14li. No significant GeV emission is found, and an upper limit of erg s−1 (at 95% confidence level) is obtained for the first 107 s after the disruption.
Correlation between the photon index and X-ray luminosity of black hole X-ray binaries and active galactic nuclei: observations and interpretation

We investigate the observed correlation between the 2-10 keV X-ray luminosity (in unit of the Eddington luminosity; l(X) L-X/L-Edd) and the photon index (Gamma) of the X-ray spectrum for both black hole X-ray binaries (BHBs) and active galactic nuclei (AGNs). We construct a large sample, with 10(-9) less than or similar to l(X) less than or similar to 10(-1). We find that Gamma is positively and negatively correlated with l(X) when l(X) greater than or similar to 10(-3) and 10(-65) less than or similar to l(X) less than or similar to 10(-3), respectively, while Gamma is nearly a constant when l(X) less than or similar to 10(-6.5). We explain the above correlation in the framework of a coupled hot accretion flow-jet model. The radio emission always comes from the jet while the X-ray emission comes from the accretion flow and jet when l(X) is above and below 10(-6.5), respectively. More specifically, we assume that with the increase of mass accretion rate, the hot accretion flow develops into a clumpy and further a disc-corona two-phase structure because of thermal instability. We argue that such kind of two-phase accretion flow can explain the observed positive correlation.
Schrödinger’s box: an artifact to study the limits of plausibility in auditory augmentations

For every physical interaction with our environment, we have some expectations concerning the resulting sound. As these expectations are quite rough, the auditory feedback can be modulated to convey additional information, without restricting the object’s original purpose. Such auditory augmentation is calm and unobtrusive as long it stays plausible with respect to the performed action. The plausibility range defines a hard limit for the information capacity of the auditory display. In order to maximize the information capacity of auditory augmentations, an estimate of the plausibility range of augmented auditory feedback is required. Here we present Schrödinger’s box, a mobile hardware- and software-platform that is designed for exploring the limits of plausibility of auditory feedback for unknown sounding objects. It renders augmented auditory feedback for its one and only affordance: striking it with a mallet. While hiding all electronics from the users, it meets the extreme requirements of latency that are necessary so that the original auditory feedback is effectively masked by the synthetic auditory feedback. With Schrödinger’s box, we now have a valuable research tool, not only for optimizing auditory augmentations, but also for investigating the plausibility of auditory feedback in general.
Rail Detection: An Efficient Row-based Network and a New Benchmark

Rail detection, essential for railroad anomaly detection, aims to identify the railroad region in video frames. Although various studies on rail detection exist, neither an open benchmark nor a high-speed network is available in the community, making al- gorithm comparison and development difficult. Inspired by the growth of lane detection, we propose a rail database and a row- based rail detection method. In detail, we make several contribu- tions: (i) We present a real-world railway dataset, Rail-DB, with 7432 pairs of images and annotations. The images are collected from different situations in lighting, road structures, and views. The rails are labeled with polylines, and the images are catego- rized into nine scenes. The Rail-DB is expected to facilitate the improvement of rail detection algorithms. (ii) We present an ef- ficient row-based rail detection method, Rail-Net, containing a lightweight convolutional backbone and an anchor classifier. Specif- ically, we formulate the process of rail detection as a row-based selecting problem. This strategy reduces the computational cost compared to alternative segmentation methods. (iii) We evaluate the Rail-Net on Rail-DB with extensive experiments, including cross-scene settings and network backbones ranging from ResNet to Vision Transformers. Our method achieves promising perfor- mance in terms of both speed and accuracy. Notably, a lightweight version could achieve 92.77% accuracy and 312 frames per second. The Rail-Net outperforms the traditional method by 50.65% and the segmentation one by 5.86%. The database and code are available at: https://github.com/Sampson-Lee/Rail-Detection.
Infrared Space Observatory Observations of Molecular Hydrogen in HH 54:Measurement of a Nonequilibrium Ratio of Ortho- to Para-H2

We have detected the S(1), S(2), S(3), S(4), and S(5) pure rotational lines of molecular hydrogen toward the outflow source HH 54 using the Short Wavelength Spectrometer on board the Infrared Space Observatory. The observed H2 line ratios indicate the presence of warm molecular gas with an H2 density of at least 105 cm-3 and a temperature ~650 K in which the ratio of ortho- to para-H2 is only 1.2±0.4, significantly smaller than the equilibrium ratio of 3 expected in gas at that temperature. These observations imply that the measured ratio of ortho- to para-H2 is the legacy of an earlier stage in the thermal history of the gas when the gas had reached equilibrium at a temperature ≲90 K. Based upon the expected timescale for equilibration, we argue that the nonequilibrium ratio of ortho- to para-H2 observed in HH 54 serves as a chronometer that places a conservative upper limit of ~5000 yr on the period for which the emitting gas has been warm. The S(2)/S(1) and S(3)/S(1) H2 line ratios measured toward HH 54 are consistent with recent theoretical models of Timmermann for the conversion of para- to ortho-H2 behind slow, C-type shocks, but only if the preshock ratio of ortho- to para-H2 was ≲0.2.
Model of Online Loan Overdue Prediction Using Fuzzy Method

Online loan has achieved great development in recent years, which benefit to both sides of lending. In this paper, we analysis the risk of lenders, using machine learning approach to predict the performances of borrower. We play the role of clearing house, use fragmentary repayment records to predict borrowers’ credit scores. We start research from the bank statement of clearing house, evaluate borrowers’ credit scores in 2 steps. First, the records in bank statement of clearing house classify to correspond period of lease contract; second, the attributes of person are extracted from data, train models, and predict the person's repayment. Our contribution in this paper are: we first start credit score predict research from bank statement of clearing house, we predict the borrowers’ action
MVSPlenOctree: Fast and Generic Reconstruction of Radiance Fields in PlenOctree from Multi-view Stereo

We present MVSPlenOctree, a novel approach that can efficiently reconstruct radiance fields for view synthesis. Unlike previous scene-specific radiance fields reconstruction methods, we present a generic pipeline that can efficiently reconstruct 360-degree-renderable radiance fields via multi-view stereo (MVS) inference from tens of sparse-spread out images. Our approach leverages variance-based statistic features for MVS inference, and combines this with image based rendering and volume rendering for radiance field reconstruction. We first train a MVS Machine for reasoning scene's density and appearance. Then, based on the spatial hierarchy of the PlenOctree and coarse-to-fine dense sampling mechanism, we design a robust and efficient sampling strategy for PlenOctree reconstruction, which handles occlusion robustly. A 360-degree-renderable radiance fields can be reconstructed in PlenOctree from MVS Machine in an efficient single forward pass. We trained our method on real-world DTU, LLFF datasets, and synthetic datasets. We validate its generalizability by evaluating on the test set of DTU dataset which are unseen in training. In summary, our radiance field reconstruction method is both efficient and generic, a coarse 360-degree-renderable radiance field can be reconstructed in seconds and a dense one within minutes. Please visit the project page for more details: https://derry-xing.github. io/projects/MVSPlenOctree.
IMuR 2022: Introduction to the 2nd Workshop on Interactive Multimedia Retrieval

The retrieval of multimedia content remains a difficult problem where a high accuracy or specificity can often only be achieved interactively, with a user working closely and iteratively with a retrieval system. While there exist several venues for the exchange of insights in the area of information retrieval in general and multimedia retrieval specifically, there is little discussion on such interactive retrieval approaches. The Workshop on Interactive Multimedia Retrieval offers such a venue. Held for the 2nd time in 2022, it attracted a diverse set of contributions, six of which were accepted for presentation. The following provides a brief overview of the workshop itself as well as the contributions of 2022.
Stacking based prediction of COVID-19 Pandemic by integrating infectious disease dynamics model and traditional machine learning

Accurate prediction of 2019 novel coronavirus diseases (COVID-19) has been playing an important role in making more effective prevention and control policies during pandemic crises. The aim of this paper was to develop an innovative stacking based prediction of COVID-19 pandemic cumulative confirmed cases (StackCPPred) by integrating infectious disease dynamics model and traditional machine learning. Based on population migration characteristics, five feature indicators were first extracted from the population flow data in the early stage of this epidemic, which were collected from the National Health Commission of the People's Republic of China. Then, stacking based ensemble learning (SEL) model was established for COVID-19 prediction using traditional machine learning, including the multiple linear regression (MLR) and the tree regression model (XGBoost and LightGBM). By introducing the variable "death state", an improved Susceptible-Infected-Recovered (ISIR) model was established. Finally, a hybrid model, StackCPPred was proposed by incorporating the ISIR model outputs and the five feature indicators into the SEL model. Real data on population movements and daily cumulative number of newly confirmed cases across the country from January 23 to February 6 were used to validate our model. The results positively proved that the proposed StackCPPred model outperformed the existing models for COVID-19 prediction, as quantified by the root mean square error (RMSE), the root mean square logarithmic error (RMSLE) and the coefficient of determination (R2) (∼1841 persons, ∼0.1 and >0.9, respectively). Furthermore, this study confirms the validity and usefulness of the StackCPPred model for COVID-19 prediction.
The Legacy of the Danish 'Church Fathers' N.F.S. Grundtvig and S.A. Kierkegaard in 11, 955 Contemporary Sermons

In this paper, we investigate representations of the two influential Danish nineteenth-century theologians N.F. S. Grundtvig (1783-1872) and S.A. Kierkegaard (1813–1855) in a text corpus of 11,955 modern-day sermons from the Evangelical-Lutheran Church in Denmark (ELCD). Through a word embedding analysis we map the semantic habitat of the two theologians individually and in relation to key theological concepts. The study thus illuminates the reception and role of these historical figures in the ELCD today.
The basic heart anatomy and physiology from the cardiologist's perspective: Toward a better understanding of left ventricular mechanics, systolic, and diastolic function

A comprehensive understanding of the cardiac structure–function relationship is essential for proper clinical cardiac imaging. This review summarizes the basic heart anatomy and physiology from the perspective of a heart imager focused on myocardial mechanics. The main issues analyzed are the left ventricular (LV) architecture, the LV myocardial deformation through the cardiac cycle, the LV diastolic function basic parameters and the basic parameters of the LV deformation used in clinical practice for the LV function assessment.
R-FEC: RL-based FEC Adjustment for Better QoE in WebRTC

The demand for video conferencing applications has seen explosive growth while users still often face unsatisfactory quality of experience (QoE). Video conferencing applications adopt Forward Error Correction (FEC) as a recovery mechanism to meet tight latency requirements and overcome packet losses prevalent in the network. However, many studies mainly focused on video rate control by neglecting the complex interactions of this video recovery mechanism on the rate control and its impact on the user QoE. Deciding the right amount of FEC for the current video rate under a dynamically changing network environment is not straightforward. For instance, the higher FEC may enhance the tolerance to packet losses, but it may increase latency due to FEC processing overhead and hurt the video quality due to the additional bandwidth used for FEC. To address this issue, we propose R-FEC which is a reinforcement learning (RL) based framework for video and FEC bitrate decisions in video conferencing. R-FEC aims to improve overall QoE by automatically learning through the results of past decisions and adjusting video and FEC bitrates to maximize the user QoE while minimizing the congestion in the network. Our experiments show that R-FEC outperforms the state-of-the-art solutions in video conferencing, with up to 27% improvement in its video rate and 6dB PSNR improvement in video quality over the default WebRTC.
DisCo: Disentangled Implicit Content and Rhythm Learning for Diverse Co-Speech Gestures Synthesis

Current co-speech gestures synthesis methods struggle with generating diverse motions and typically collapse to single or few frequent motion sequences, which are trained on original data distribution with customized models and strategies. We tackle this problem by temporally clustering motion sequences into content and rhythm segments and then training on content-balanced data distribution. In particular, by clustering motion sequences, we have observed for each rhythm pattern, some motions appear frequently, while others appear less. This imbalance results in the difficulty of generating low frequent occurrence motions and it cannot be easily solved by resampling, due to the inherent many-to-many mapping between content and rhythm. Therefore, we present DisCo, which disentangles motion into implicit content and rhythm features by contrastive loss for adopting different data balance strategies. Besides, to model the inherent mapping between content and rhythm features, we design a diversity-and-inclusion network (DIN), which firstly generates content features candidates and then selects one candidate by learned voting. Experiments on two public datasets, Trinity and S2G-Ellen, justify that DisCo generates more realistic and diverse motions than state-of-the-art methods. Code and data are available at https://pantomatrix.github. io/DisCo/
CALET: a high energy astroparticle physics experiment on the ISS

CALET (CALorimetric Electron Telescope) is a high energy astroparticle physics experiment planned for a long exposure mission aboard the International Space Station (ISS) by the Japanese Aerospace Exploration Agency, in collaboration with the Italian Space Agency (ASI) and NASA. The main science goal is high precision measurements of the inclusive electron (+positron) spectrum below 1 TeV and the exploration of the energy region above 1 TeV, where the shape of the high end of the spectrum might unveil the presence of nearby sources of acceleration. CALET has been designed to achieve a large proton rejection capability (>10$^5$) with a fine grained imaging calorimeter (IMC) followed by a total absorption calorimeter (TASC), for a total thickness of 30 X$_{0}$ and 1.3 proton interaction length. With an excellent energy resolution and a lower background contamination with respect to previous experiments, CALET will search for possible spectral signatures of dark matter with both electrons and gamma rays. CALET will also measure the high energy spectra and relative abundance of cosmic nuclei from proton to iron and detect trans-iron elements up to Z$\sim$40. The charge identification of individual nuclear species is performed by a dedicated module (CHD) and by multiple dE/dx measurements in the IMC. With a large exposure and high energy resolution, CALET will be able to verify and complement the observations of CREAM, PAMELA and AMS-02 on a possible deviation from a pure power-law of proton and He spectra in the few hundred GeV region and to extend the study to the multi-TeV region. CALET will also contribute to clarify the present experimental picture on the energy dependence of the boron/carbon ratio, below and above 1 TeV/n, thereby providing valuable information on cosmic-ray propagation in the galaxy. Gamma-ray transients will be studied with a dedicated Gamma-ray Burst Monitor (GBM).
Semantic-aware Responsive Listener Head Synthesis

Audience providing proper reaction during a conversation can bring positive impact to speaker, which is significant to digital human and social agent areas. Given information sent by speaker, responsive listener head synthesis task aims to generate corresponding listener behaviours such as nodding, thinking and smiling. A common method is to build listener responsive pattern by analyzing acoustic and facial feature of speaker. However, it is hard to understand what speaker means, purely based on acoustic and facial feature since numerous message is buried in language. Traditional method may lead to similar results ignoring the diversity of input. Therefore, in this paper we presents a new Semantic-aware Responsive Listener Head Synthesis (SaRLHS) approach by considering semantic information lied in language patterns in addition to acoustic and facial feature. Besides, we implement a post-face enhancement process to increase the visual effects. Moreover, we won the People's Selection Awards and the second place on Grand Challenges of ACM 2022 conference.
Higher-dimensional charged black hole solutions with a nonlinear electrodynamics source

We obtain electrically charged black hole solutions of the Einstein equations in arbitrary dimensions with a nonlinear electrodynamics source. The matter source is derived from a Lagrangian given by an arbitrary power of the Maxwell invariant. The form of the general solution suggests a natural partition for the different ranges of this power. For a particular range, we exhibit a class of solutions whose behavior resembles the standard Reissner–Nordström black holes. There also exists a range for which the black hole solutions approach asymptotically the Minkowski spacetime slower than the Schwarzschild spacetime. We have also found a family of non-asymptotical flat black hole solutions with an asymptotic behavior growing slower than the Schwarzschild–(anti)-de Sitter spacetime. In odd dimensions, there exists a critical value of the exponent for which the metric involves a logarithmic dependence. This critical value corresponds to the transition between the standard behavior and the solution decaying to Minkowski slower than the Schwarzschild spacetime.
PDD-GAN: Prior-based GAN Network with Decoupling Ability for Single Image Dehazing

Single image dehazing is a challenging vision problem aiming to provide clear images for downstream computer vision applications (e.g., semantic segmentation, object detection, and super resolution). Most existing methods leverage the physical scattering model or convolutional neural networks (CNNs) for haze removal, which however ignore the complementary advantages between each other. Especially lacking marginal and visual prior instructions, CNN-based methods still have gaps in details and color recovery. To solve these, we propose a Prior-based with Decoupling ability Dehazing GAN Network (PDD-GAN), which is based on PeleetNet and attached with an attention module (CBAM). The prior-based decoupling approach consists of two parts: high and low frequency filtering and HSV contrastive loss. We process the image via a band-stop filter and add it as the fourth channel of data (RGBFHL) to decouple the hazy image at the structural level. Besides, a novel prior loss with contrastive regularization is proposed at the visual level. Sufficient experiments are carried out to demonstrate that PDD-GAN outperforms state-of-the-art methods by up to 0.86db in PSNR. In particular, extensive experiments indicate that RGBFHL increases by 0.99db compared with the original three-channel data (RGB) and the extra HSV prior loss escalates by 2.0db. Above all, our PDD-GAN indeed has the decoupling ability and improves the dehazing results.
Representation Learning through Multimodal Attention and Time-Sync Comments for Affective Video Content Analysis

Although temporal patterns inherent in visual and audio signals are crucial for affective video content analysis, they have not been thoroughly explored yet. In this paper, we propose a novel Temporal-Aware Multimodal (TAM) method to fully capture the temporal information. Specifically, we design a cross-temporal multimodal fusion module that applies attention-based fusion to different modalities within and across video segments. As a result, it fully captures the temporal relations between different modalities. Furthermore, a single emotion label lacks supervision for learning representation of each segment, making temporal pattern mining difficult. We leverage time-synchronized comments (TSCs) as auxiliary supervision, since these comments are easily accessible and contain rich emotional cues. Two TSC-based self-supervised tasks are designed: the first aims to predict the emotional words in a TSC from video representation and TSC contextual semantics, and the second predicts the segment in which the TSC appears by calculating the correlation between video representation and TSC embedding. These self-supervised tasks are used to pre-train the cross-temporal multimodal fusion module on a large-scale video-TSC dataset, which is crawled from the web without labeling costs. These self-supervised pre-training tasks prompt the fusion module to perform representation learning on segments including TSC, thus capturing more temporal affective patterns. Experimental results on three benchmark datasets show that the proposed fusion module achieves state-of-the-art results in affective video content analysis. Ablation studies verify that after TSC-based pre-training, the fusion module learns more segments' affective patterns and achieves better performance.