Miracle-dz/UniFAR-FTUs huggingface dataset:
First detection of X-ray line emission from Type IIn supernova 1978K with XMM-Newton’s RGS

We report on robust measurements of elemental abundances of the Type IIn supernova SN 1978K, based on the high-resolution X-ray spectrum obtained with the Reflection Grating Spectrometer (RGS) onboard XMM-Newton. The RGS clearly resolves a number of emission lines, including N Ly$\alpha$, O Ly$\alpha$, O Ly$\beta$, Fe xvii, Fe xviii, Ne He$\alpha$, and Ne Ly$\alpha$ for the first time from SN 1978K. The X-ray spectrum can be represented by an absorbed, two-temperature thermal emission model, with temperatures of $kT \sim 0.6$ keV and 2.7 keV. The elemental abundances are obtained to be N $=$$2. 36_{{-0.80}}^{{+0.88}}$, O $=$$0.20 \pm {0.05}$, Ne $=$$0.47 \pm {0.12}$, Fe $=$$0. 15_{{-0.02}}^{{+0.01}}$ times the solar values. The low metal abundances except for N show that the X-ray emitting plasma originates from the circumstellar medium blown by the progenitor star. The abundances of N and O are far from the CNO-equilibrium abundances expected for the surface composition of a luminous blue variable, and resemble the H-rich envelope of less massive stars with masses of 10–25$\, M_{\odot }$. Together with other peculiar properties of SN 1978K, i.e., a low expansion velocity of 500–1000 km s$^{-1}$ and SN IIn-like optical spectra, we propose that SN 1978K is a result of either an electron-capture SN from a super asymptotic giant branch star, or a weak Fe core-collapse explosion of a relatively low-mass ($\sim \! \! 10\, M_{\odot }$) or high-mass ($\sim$20–25$\, M_{\odot }$) red supergiant star. However, these scenarios cannot naturally explain the high mass-loss rate of the order of $\dot{M} \sim 10^{-3}\, M_{\odot }\:{\rm yr^{-1}}$ over $\gtrsim$1000 yr before the explosion, which is inferred by this work as well as many other earlier studies. Further theoretical studies are required to explain the high mass-loss rates at the final evolutionary stages of massive stars.
Data Curves Clustering Using Common Patterns Detection

For the past decades we have experienced an enormous expansion of the accumulated data that humanity produces. Daily a numerous number of smart devices, usually interconnected over internet, produce vast, real-values datasets. Time series representing datasets from completely irrelevant domains such as finance, weather, medical applications, traffic control etc. become more and more crucial in human day life. Analyzing and clustering these time series, or in general any kind of curves, could be critical for several human activities. In the current paper, the new Curves Clustering Using Common Patterns (3CP) methodology is introduced, which applies a repeated pattern detection algorithm in order to cluster sequences according to their shape and the similarities of common patterns between time series, data curves and eventually any kind of discrete sequences. For this purpose, the Longest Expected Repeated Pattern Reduced Suffix Array (LERP-RSA) data structure has been used in combination with the All Repeated Patterns Detection (ARPaD) algorithm in order to perform highly accurate and efficient detection of similarities among data curves that can be used for clustering purposes and which also provides additional flexibility and features.
INDOOR LOCALIZATION SYSTEM OF ROS MOBILE ROBOT BASED ON VISIBLE LIGHT COMMUNICATION

In this paper, an indoor robot localization system based on Robot Operating System (ROS) and visible light communication (VLC) is presented. On the basis of our previous work, we innovatively designed a VLC localization and navigation package based on Robot Operating System (ROS), which contains the LED-ID detection and recognition method, the video target tracking algorithm and the double-lamp positioning algorithm. This package exploited the principle of double-lamp positioning and the loose coupling characteristics of the ROS system, which is implemented by loosely coupled ROS nodes. Consequently, this paper combines ROS and VLC, aiming at promoting the application of VLC positioning in mature robotic systems. Moreover, it pushed forward the development of localization application based on VLC technology and lays a foundation for transplanting to other ROS robot platforms. Experimental results show that the proposed system can provide indoor localization within 1 cm and possesses a good real-time performance which takes only 0.4 seconds for one-time positioning. And if a high-performance laptop is used, the single positioning time can be reduced to 0.08 seconds. Therefore, this study confirms the practical application and the superior performance of VLC technology in ROS robot, showing the great potential of VLC localization. T he video demo of the proposed robot positioning system based on VLC can be seen in *
Uncertainty Quantification for Materials Properties in Density Functional Theory with k-Point Density

Many computational databases emerged over the last five years that report material properties calculated with density functional theory. The properties in these databases are commonly calculated to a precision that is set by choice of the basis set and the k-point density for the Brillouin zone integration. We determine how the precision of properties obtained from the Birch equation of state for 29 transition metals and aluminum in the three common structures -- fcc, bcc, and hcp -- correlate with the k-point density and the precision of the energy. We show that the precision of the equilibrium volume, bulk modulus, and the pressure derivative of the bulk modulus correlate comparably well with the k-point density and the precision of the energy, following an approximate power law. We recommend the k-point density as the convergence parameter because it is computationally efficient, easy to use as a direct input parameter, and correlates with property precision at least as well as the energy precision. We predict that common k-point density choices in high throughput DFT databases result in precision for the volume of 0.1%, the bulk modulus of 1%, and the pressure derivative of 10%.
Macromolecule Classification Based on the Amino-acid Sequence

Deep learning is playing a vital role in every field which involves data. It has emerged as a strong and efficient framework that can be applied to a broad spectrum of complex learning problems which were difficult to solve using traditional machine learning techniques in the past. In this study we focused on classification of protein sequences with deep learning techniques. The study of amino acid sequence is vital in life sciences. We used different word embedding techniques from Natural Language processing to represent the amino acid sequence as vectors. Our main goal was to classify sequences to four group of classes, that are DNA, RNA, Protein and hybrid. After several tests we have achieved almost 99% of train and test accuracy. We have experimented on CNN, LSTM, Bidirectional LSTM, and GRU.
Statistical Inference for High-Dimensional Matrix-Variate Factor Models

ABSTRACT This article considers the estimation and inference of the low-rank components in high-dimensional matrix-variate factor models, where each dimension of the matrix-variates (p × q) is comparable to or greater than the number of observations (T). We propose an estimation method called α-PCA that preserves the matrix structure and aggregates mean and contemporary covariance through a hyper-parameter α. We develop an inferential theory, establishing consistency, the rate of convergence, and the limiting distributions, under general conditions that allow for correlations across time, rows, or columns of the noise. We show both theoretical and empirical methods of choosing the best α, depending on the use-case criteria. Simulation results demonstrate the adequacy of the asymptotic results in approximating the finite sample properties. The α-PCA compares favorably with the existing ones. Finally, we illustrate its applications with a real numeric dataset and two real image datasets. In all applications, the proposed estimation procedure outperforms previous methods in the power of variance explanation using out-of-sample 10-fold cross-validation. Supplementary materials for this article are available online.
Reinforcement Learning via Fenchel-Rockafellar Duality

We review basic concepts of convex duality, focusing on the very general and supremely useful Fenchel-Rockafellar duality. We summarize how this duality may be applied to a variety of reinforcement learning (RL) settings, including policy evaluation or optimization, online or offline learning, and discounted or undiscounted rewards. The derivations yield a number of intriguing results, including the ability to perform policy evaluation and on-policy policy gradient with behavior-agnostic offline data and methods to learn a policy via max-likelihood optimization. Although many of these results have appeared previously in various forms, we provide a unified treatment and perspective on these results, which we hope will enable researchers to better use and apply the tools of convex duality to make further progress in RL.
Regression and Learning with Pixel-wise Attention for Retinal Fundus Glaucoma Segmentation and Detection

Observing retinal fundus images by an ophthalmologist is a major diagnosis approach for glaucoma. However, it is still difficult to distinguish the features of the lesion solely through manual observations, especially, in glaucoma early phase. In this paper, we present two deep learning-based automated algorithms for glaucoma detection and optic disc and cup segmentation. We utilize the attention mechanism to learn pixel-wise features for accurate prediction. In particular, we present two convolutional neural networks that can focus on learning various pixel-wise level features. In addition, we develop several attention strategies to guide the networks to learn the important features that have a major impact on prediction accuracy. We evaluate our methods on the validation dataset and The proposed both tasks' solutions can achieve impressive results and outperform current state-of-the-art methods. \textit{The code is available at \url{this https URL}}.
Real-time Grasp Pose Estimation for Novel Objects in Densely Cluttered Environment

Grasping of novel objects in pick and place applications is a fundamental and challenging problem in robotics, specifically for complex-shaped objects. It is observed that the well-known strategies like i) grasping from the centroid of object and ii) grasping along the major axis of the object often fails for complex-shaped objects. In this paper, a realtime grasp pose estimation strategy for novel objects in robotic pick and place applications is proposed. The proposed technique estimates the object contour in the point cloud and predicts the grasp pose along with the object skeleton in the image plane. The technique is tested for the objects like ball container, hand weight, tennis ball and even for complex shape objects like blower (non-convex shape). It is observed that the proposed strategy performs very well for complex shaped objects and predicts the valid grasp configurations in comparison with the above strategies. The experimental validation of the proposed grasping technique is tested in two scenarios, when the objects are placed distinctly and when the objects are placed in dense clutter. A grasp accuracy of 88.16% and 77.03% respectively are reported. All the experiments are performed with a real UR10 robot manipulator along with WSG-50 two-finger gripper for grasping of objects.
The Assurance Monitor Pattern

Some applications require an assurance that certain criteria are violated with only low probability. An alert is generated when the current course of action is likely to violate assurance criteria and the alert results in corrective action. Assurance monitors fuse information from multiple data streams generated by sensors and other sources to estimate the probability distribution of system trajectories. This distribution is used to determine whether an assurance constraint is likely to be violated. The acquisition of data requires resources such as energy, computational power and communication bandwidth which are scarce in many applications. At each point in time the system must decide whether to expend these resources to get more data to improve the confidence in the probability distribution of trajectories. This paper presents a software-design pattern for assurance monitoring and gives examples of its uses, including an application of the pattern to the problem of autonomous navigation of a drone which is required to avoid no-fly zones while using limited resources.
xGASS: H i Fueling of Star Formation in Disk-dominated Galaxies

We introduce a method to estimate the H i mass within the optical radius of disk galaxies from integrated H i spectra, with an uncertainty of 0.09 dex. We use these estimates to study how inner H i fuels star formation in late-type disk galaxies. We find that star formation rate (SFR) at a given stellar mass (M*) is well correlated with the inner H i surface density ( ) and inner H i mass-to-stellar mass ratio. For the massive (M* > 1010M⊙) disk galaxies, higher SFR at a given stellar mass is also related to higher efficiency of converting inner H i to molecular gas, but no such correlation is found for the total H i mass. The highest and the fastest depletion of the total neutral gas within the optical disks are found in the most compact and star-forming disk galaxies at a given stellar mass. These results highlight the important role of inner H i as an intermediate step of fueling star formation in disk galaxies.
A parsec-scale wobbling jet in the high-synchrotron peaked blazar PG 1553+113

Context. The detection of quasi-periodic variability in active galactic nuclei in general, and in blazars in particular, is key to our understanding of the origin and nature of these objects as well as their cosmological evolution. PG 1553+113 is the first blazar showing an approximately two-year quasi-periodic pattern in its γ-ray light curve, which is also revealed at optical frequencies. Aims. Such quasi-periodicity might have a geometrical origin, possibly related to the precessing nature of the jet, or could be intrinsic to the source and related to pulsational accretion flow instabilities. In this work we investigate and characterise the high-resolution radio emission properties of PG 1553+113 on parsec scales in order to differentiate between these different physical scenarios. Methods. We monitored the source with the very long baseline array (VLBA) at 15, 24, and 43 GHz during an entire cycle of γ-ray activity in the period 2015–2017, with a cadence of about 2 months, both in total and polarised intensity. We constrained the jet position angle across the different observing epochs by investigating the total intensity ridge lines. Results. We find a core-dominated source with a limb-brightened jet structure extending for ∼1.5 mas in the northeast direction whose position angle varies in time in the range ∼40°−60°. No clear periodic pattern can be recognized in the VLBA light curves during 2015–2017 or in the 15 GHz Owens Valley Radio Observatory light curve during the period 2008–2018. The core region polarisation percentage varies in the range ∼1−4%, and the polarisation angle varies from being roughly parallel to roughly transverse to the jet axis. We estimate a rotation measure value in the core region of ∼−1.0 ± 0.4 × 104 rad m−2. The brightness temperature (TB) is found to decrease as the frequency increases with an intrinsic value of ∼1.5 × 1010 K and the estimated Doppler factor is ∼1.4. Conclusions. Although the jet wobbling motion indicates that geometrical effects can produce an enhanced emission through the Doppler boosting modulation, additional mechanisms are required in order to account for the quasi-periodic variability patterns observed in γ rays. The intrinsic TB value indicates that the total energy in the core region is dominated by the magnetic field.
Federated Learning for Localization: A Privacy-Preserving Crowdsourcing Method

Received Signal Strength (RSS) fingerprint-based localization has attracted a lot of research effort and cultivated many commercial applications of location-based services due to its low cost and ease of implementation. Many studies are exploring the use of deep learning (DL) algorithms for localization. DL's ability to extract features and to classify autonomously makes it an attractive solution for fingerprint-based localization. These solutions require frequent retraining of DL models with vast amounts of measurements. Although crowdsourcing is an excellent way to gather immense amounts of data, it jeopardizes the privacy of participants, as it requires to collect labeled data at a centralized server. Recently, federated learning has emerged as a practical concept in solving the privacy preservation issue of crowdsourcing participants by performing model training at the edge devices in a decentralized manner; the participants do not expose their data anymore to a centralized server. This paper presents a novel method utilizing federated learning to improve the accuracy of RSS fingerprint-based localization while preserving the privacy of the crowdsourcing participants. Employing federated learning allows ensuring \emph{preserving the privacy of user data} while enabling an adequate localization performance with experimental data captured in real-world settings. The proposed method improved localization accuracy by 1.8 meters when used as a booster for centralized learning and achieved satisfactory localization accuracy when used standalone.
DAF-NET: a saliency based weakly supervised method of dual attention fusion for fine-grained image classification

Fine-grained image classification is a challenging problem, since the difficulty of finding discriminative features. To handle this circumstance, basically, there are two ways to go. One is use attention based method to focus on informative areas, while the other one aims to find high order between features. Further, for attention based method there are two directions, activation based and detection based, which are proved effective by scholars. However ,rare work focus on fusing two types of attention with high order feature. In this paper, we propose a novel DAF method which fuse two types of attention and use them to as PAF(part attention filter) in deep bilinear transformation module to mine the relationship between separate parts of an object. Briefly, our network constructed by a student net who attempt to output two attention maps and a teacher net uses these two maps as empirical information to refine the result. The experiment result shows that only student net could get 87.6% accuracy in CUB dataset while cooperating with teacher net could achieve 89.1% accuracy.
Li abundances for solar twins in the open cluster M67

We determine lithium (Li) abundances for solar twins in the M67 open cluster to add valuable information about the correlation between Li depletion and stellar age and, then, better understand stellar structure and evolution. We use high resolution and good signal-to-noise ratio spectra to characterize Li depletion in three solar twins from M67, using spectral synthesis in the region of the asymmetric 6707.75 Å Li i feature. The mean Li abundance value of A(Li) = 1.6 ± 0.2 dex for our sample of M67 solar twins (our three stars plus a fourth solar twin from a previous analysis in the literature) presents Li abundance expected for its age. Also, the scatter estimated from the standard deviation of the Li abundances in this work is similar to the typical scatter found in a sample of field solar twins presented in the literature.
AD-VO: Scale-Resilient Visual Odometry Using Attentive Disparity Map

Visual odometry is an essential key for a localization module in SLAM systems. However, previous methods require tuning the system to adapt environment changes. In this paper, we propose a learning-based approach for frame-to-frame monocular visual odometry estimation. The proposed network is only learned by disparity maps for not only covering the environment changes but also solving the scale problem. Furthermore, attention block and skip-ordering scheme are introduced to achieve robust performance in various driving environment. Our network is compared with the conventional methods which use common domain such as color or optical flow. Experimental results confirm that the proposed network shows better performance than other approaches with higher and more stable results.
Accuracy of the analytical escape rate for a cusp barrier in the overdamping regime

For the first time, the accuracy of the approximate analytical Kramers formula for the thermal decay rate over a cusp barrier, RK, is checked numerically for the overdamping regime. The numerical quasistationary rate, RD, which is believed to be exact within the statistical errors is evaluated by means of computer modeling of the stochastic Langevin-type dynamical equations. The agreement between RK and RD significantly depends upon the friction strength and the height of the barrier in comparison to the thermal energy. The difference between RK and RD decreases with the dimensionless damping parameter φ, however, does not become smaller than 10-20%. The unexpected growth of the difference between RK and RD with the governing parameter is observed.
On the primary coverings of finite solvable and symmetric groups

Abstract A primary covering of a finite group 𝐺 is a family of proper subgroups of 𝐺 whose union contains the set of elements of 𝐺 having order a prime power. We denote by σ 0 ⁢ ( G ) \sigma_{0}(G) the smallest size of a primary covering of 𝐺 and call it the primary covering number of 𝐺. We study this number and compare it with its analogue σ ⁢ ( G ) \sigma(G) , the covering number, for the classes of groups 𝐺 that are solvable and symmetric.
Quantum Advantage in Shared Randomness Processing

Randomness appears both in classical stochastic physics and in quantum mechanics. A number of communication models and other information protocols entail sharing randomness among distant parties. Here we report a computational scenario of shared randomness processing where quantum sources manifest clear-cut precedence over the corresponding classical counterparts. The advantage is established in a resource theoretic set-up which we formalize in this work. The classical mutual information turns out to be a faithful resource quantifier of shared randomness although it does not sufficiently characterize all possible transitions among the resources. Quantum theory exhibits advantage in shared randomness processing in the sense that the set of resourceful states obtained under free operation from a bipartite quantum system is strictly larger than the set obtained from an analogous classical system. Interestingly, the quantum advantage can be manifested in the optimal payoff of a two players co-operative game -- the `non-monopolize social subsidy' game. We also show that the quantum states resulting to the desired advantage necessitates nonclassicality in the form of quantum discord. We then address the task of distributing shared randomness between two parties. Quite interestingly, it turns out that noisy quantum channel can exhibit advantage even when it has zero quantum capacity and classical capacity much less than the corresponding classical channel. The imperfect channel examples facilitates and welcomes experiment to achieve the reported quantum advantage with presently available quantum devices.