Miracle-dz/UniFAR-FTUs huggingface dataset:
Artificial Neural Network Algorithm based Skyrmion Material Design of Chiral Crystals

The model presented in this research predicts ideal chiral crystal and propose a new direction of designing chiral crystals. Skyrmions are topologically protected and structurally assymetric materials with an exotic spin composition. This work presents deep learning method for skyrmion material design of chiral crystals. This paper presents an approach to construct a probabilistic classifier and an Artificial Neural Network(ANN) from a true or false chirality dataset consisting of chiral and achiral compounds with 'A' and 'B' type elements. A quantitative predictor for accuracy of forming the chiral crystals is illustrated. The feasibility of ANN method is tested in a comprehensive manner by comparing with probalistic classifier method. Throughout this manuscript we present deep learnig algorithm design with modelling and simulations of materials. This research work elucidated paves a way to develop sophisticated software tool to make an indicator of crystal design.
Construct Dynamic Graphs for Hand Gesture Recognition via Spatial-Temporal Attention

We propose a Dynamic Graph-Based Spatial-Temporal Attention (DG-STA) method for hand gesture recognition. The key idea is to first construct a fully-connected graph from a hand skeleton, where the node features and edges are then automatically learned via a self-attention mechanism that performs in both spatial and temporal domains. We further propose to leverage the spatial-temporal cues of joint positions to guarantee robust recognition in challenging conditions. In addition, a novel spatial-temporal mask is applied to significantly cut down the computational cost by 99%. We carry out extensive experiments on benchmarks (DHG-14/28 and SHREC'17) and prove the superior performance of our method compared with the state-of-the-art methods. The source code can be found at this https URL.
Sign switching of dimer correlations in 
SrCu2(BO3)2
 under hydrostatic pressure

Magnetic and vibrational excitations in SrCu$_2$(BO$_3$)$_2$ are studied using Raman spectroscopy at hydrostatic pressures up to 34 kbar and temperatures down to 2.6 K. The frequency of a particular optical phonon, the so-called pantograph mode, shows a very strong anomalous temperature dependence below about 40 K. We link the magnitude of the effect to the magnetic exchange energy on the dimer bonds in the Sutherland-Shastry spin lattice in this material. The corresponding dimer spin correlations are quantitatively estimated and found to be strongly pressure dependent. At around P$_2\sim$22 kbar they switch from antiferromagnetic to being predominantly ferromagnetic.
Water masers in Compton-thick AGN

Aims. Investigations of H2O maser galaxies at X-ray energies reveal that most active galactic nuclei (AGN) associated with water masers are characterized by high levels of absorption. With the aim of finding new maser sources for possible interferometric follow-ups, we have searched for water maser emission in a well-defined sample of heavily absorbed AGN (NH > 1023 cm−2), including Compton-thick (CT) sources. Methods. Previous surveys already searched for 22 GHz water maser emission in all the galaxies in this sample. With the goal of providing a detection or a stringent upper limit on the H2O luminosity, we reobserved some of the non-detected sources with the Green Bank Telescope. A new luminous H2O maser (LH2O ∼ 200 L⊙) was detected in the mid-IR-bright Seyfert 2 galaxy IRAS 15480−0344 and then followed-up with the Very Long Baseline Array. In order to shed light on the origin of the maser (jet, outflow, or disk), we recently observed the radio continuum emission in IRAS 15480-0344 with the European VLBI network (EVN) at 1.7 and 5.0 GHz. Results. With the newly discovered megamaser in IRAS 15480−0344 revealing a narrow (∼0.6 km s−1) and a broad (∼90 km s−1) component, the maser detection rate of the CT AGN sample is 50% (18/36), which is one of the highest ever found in maser surveys. The EVN maps show two bright sources (labeled SW and NE) in the nuclear region of IRAS 15480−0344, which we interpret as jet knots tracing regions where the radio plasma impacts dense molecular clouds. The narrow maser feature is approximately at the center of the imaginary line connecting the two continuum sources, likely pinpointing the core, and might be associated with the accretion disk or a nuclear outflow. The location of the broad maser feature, instead, coincides with source NE, suggesting that the maser emission might be produced by a jet-cloud interaction, as it was proposed for NGC 1068 and Mrk 348.
Hole‐Filling Methods Using Depth and Color Information for Generating Multiview Images

This paper presents new hole‐filling methods for generating multiview images by using depth image based rendering (DIBR). Holes appear in a depth image captured from 3D sensors and in the multiview images rendered by DIBR. The holes are often found around the background regions of the images because the background is prone to occlusions by the foreground objects. Background‐oriented priority and gradient‐oriented priority are also introduced to find the order of hole‐filling after the DIBR process. In addition, to obtain a sample to fill the hole region, we propose the fusing of depth and color information to obtain a weighted sum of two patches for the depth (or rendered depth) images and a new distance measure to find the best‐matched patch for the rendered color images. The conventional method produces jagged edges and a blurry phenomenon in the final results, whereas the proposed method can minimize them, which is quite important for high fidelity in stereo imaging. The experimental results show that, by reducing these errors, the proposed methods can significantly improve the hole‐filling quality in the multiview images generated.
Sound velocity in dense stellar matter with strangeness and compact stars *

The phase state in the intermediate density range of dense matter ($\sim1-10$ times of nuclear saturation density) is both intriguing and unclear, and could have important observable effects in the present gravitational wave era of neutron stars. Since the matter density in neutron stars is in the nonperturbative interaction region, the sound velocity is expected to approach the conformal limit ($c_s/c=1/\sqrt{3}$) at high densities, and it should also fulfill the causality limit ($c_s/c<1$). However, its detailed behavior remains a hot topic of debate. We explore the general properties of the sound velocity and the adiabatic index in hybrid stars, as well as in neutron stars and quark stars. For this purpose, the bag model, the perturbation model, the equivparticle model, and the quasiparticle model are employed for the quark phase. One representative effective field theory model is used for the hardon phase. Various conditions are employed for hadron-quark phase transition and we employ an interface tension in a preferred range of $1-50~\rm MeV/fm^2$. The results are compared with various ab-initio calculations. We find a characteristic behavior of dynamical rescaling of the bag constant on the sound velocity in quark matter, which resembles that of quark deconfinement phase transition at $\sim3-7$ times of nuclear saturation density. And it leads to a much compact star with similar mass. We also propose a new class of quark star equation of states attributing the feature. The quark star equation of state model, as well as the in-medium scaling of the bag parameter in our calculation can be tested by future high-precision radius measurements of pulsar-like objects.
An Extensible Management Framework for Web Service QoS

Web service is emerging as a promising technology and attracts many attentions from industry and research domains. With the popularity of service technology, many researchers propose that not only functional but also non-functional properties, also known as quality of service (QoS), should be taken into consideration. Researches have intensively explored many QoS related problems, such as QoS-aware dynamic service selection and composition, etc. However, existing approaches emphasize on how to make use of QoS information, the technologies on collecting, storing, measuring Web service QoS information are not intensively explored. It’s obvious that these fundamental technologies are also very important for successfully building QoS related applications. To address this problem, this paper proposes an extensible QoS information management framework, analyzes the technologies included in this framework, and presents the design decisions and solutions in Software Component Library Project of Peking University. Finally, this paper describes a real case study of applying this management framework in a project of 863 Program. The case study shows that users can extend the management framework and manage Web Service QoS according to their domain-specific requirements, which evaluates the extensibility and practicability of this QoS information management framework.
Deep Learning Approaches for Image Retrieval and Pattern Spotting in Ancient Documents

This paper describes two approaches for content-based image retrieval and pattern spotting in document images using deep learning. The first approach uses a pre-trained CNN model to cope with the lack of training data, which is fine-tuned to achieve a compact yet discriminant representation of queries and image candidates. The second approach uses a Siamese Convolution Neural Network trained on a previously prepared subset of image pairs from the ImageNet dataset to provide the similarity-based feature maps. In both methods, the learned representation scheme considers feature maps of different sizes which are evaluated in terms of retrieval performance. A robust experimental protocol using two public datasets (Tobacoo-800 and DocExplore) has shown that the proposed methods compare favorably against state-of-the-art document image retrieval and pattern spotting methods.
On the disc reflection spectroscopy of NS LMXB Serpens X-1: analysis of a recent NuSTAR observation

We present NuSTAR observation of the atoll type neutron star (NS) low-mass X-ray binary (LMXB) Serpens X-1 (Ser X-1) performed on 2018 February 17. We observed Ser X-1 in a soft X-ray spectral state with 3–79 keV luminosity of LX ∼ 0.4 × 1038 erg s−1 (${\sim}23{{\ \rm per\ cent}}$ of the Eddington luminosity), assuming a distance of 7.7 kpc. A positive correlation between intensity and hardness ratio suggests that the source was in the banana branch during this observation. The broad-band 3–30 keV NuSTAR energy spectrum can be well described either by a three-component continuum model consisting of a disc blackbody, a single temperature blackbody, and a power law or by a two-component continuum model consisting of a disc blackbody and a Comptonization component. A broad iron line ∼5–8 keV and the Compton back-scattering hump peaking at ∼10–20 keV band are clearly detected in the X-ray spectrum. These features are best interpreted by a self-consistent relativistic reflection model. Fits with relativistically blurred disc reflection model suggests that the inner disc radius Rin is truncated prior to the ISCO at (1.9–2.5) RISCO (${\simeq}11.4\! -\! 15\, R_{g}\: \text{or}\: 26\! -\! 34$ km) and the accretion disc is viewed at a low inclination of i ≃ 16°−20°. The disc is likely to be truncated either by a boundary layer or by the magnetosphere. Based on the measured flux and the mass accretion rate, the maximum radial extension for the boundary layer is estimated to be ∼6.4 Rg from the NS surface. The truncated inner disc in association with pressure from a magnetic field sets an upper limit of B ≤ 1.9 × 109 G.
X-Ray Spectrum of RBS 315: Absorption or Intrinsic Curvature

X-ray absorption of high-redshift quasars is enigmatic, because it remains unclear where in the universe the absorbing gas is. If absorption occurs near the high-z host, it could help us understand early stages of galaxy formation. If it is in the intergalactic medium (IGM), it provides a unique way to probe this elusive baryon component. We report on observations of one of the brightest X-ray sources at a high redshift, RBS 315 (z = 2.69). Despite several previous analyses, no definite conclusion as to the source of the curvature in its spectrum, whether absorption or intrinsic, could be reached. We present observations by XMM-Newton’s European Photon Imaging Camera (EPIC) and Reflection Grating Spectrometers (RGS) as well as NuSTAR and Swift’s X-Ray Telescope (XRT). The XMM-Newton spectra of this source are of unprecedented quality. A purely statistical analysis of the charge-coupled device (CCD) spectra yields no clear results—the spectrum is as likely to be photoelectrically absorbed as it is to be curved at the source, and no constraint on the position of the absorber can be obtained. Assuming absorption governs the spectral curvature, the lack of absorption lines in the grating spectra indicates the absorber is not well localized in redshift space, and could be dispersed over the cosmological scales of the IGM. Intrinsic curvature, however, can not be unambiguously ruled out.
Spatio-Temporal Correlation Analysis of Online Monitoring Data for Anomaly Detection and Location in Distribution Networks

The online monitoring data in distribution networks contain rich information on the running states of the networks. By leveraging the data, this paper proposes a spatio-temporal correlation analysis approach for anomaly detection and location in distribution networks. First, spatio-temporal matrix for each feeder line in a distribution network is formulated and the spectrum of its covariance matrix is analyzed. The spectrum is complex and exhibits two aspects: 1) bulk, which arises from random noise or fluctuations and 2) spikes, which represents factors caused by anomaly signals or fault disturbances. Then, by connecting the estimation of the number of factors to the limiting empirical spectral density of covariance matrices of residuals, the spatio-temporal parameters are accurately estimated, during which free random variable techniques are used. Based on the estimators, anomaly indicators are designed to detect and locate the anomalies by exploring the variations of spatio-temporal correlations in the data. The proposed approach is sensitive to the anomalies and robust to random fluctuations, which makes it possible for detecting early anomalies and reducing false alarming rate. Case studies on both synthetic data and real-world online monitoring data verify the effectiveness and advantages of the proposed approach.
Deep Reinforcement Learning for Autonomous Internet of Things: Model, Applications and Challenges

The Internet of Things (IoT) extends the Internet connectivity into billions of IoT devices around the world, where the IoT devices collect and share information to reflect status of the physical world. The Autonomous Control System (ACS), on the other hand, performs control functions on the physical systems without external intervention over an extended period of time. The integration of IoT and ACS results in a new concept - autonomous IoT (AIoT). The sensors collect information on the system status, based on which the intelligent agents in the IoT devices as well as the Edge/Fog/Cloud servers make control decisions for the actuators to react. In order to achieve autonomy, a promising method is for the intelligent agents to leverage the techniques in the field of artificial intelligence, especially reinforcement learning (RL) and deep reinforcement learning (DRL) for decision making. In this paper, we first provide a tutorial of DRL, and then propose a general model for the applications of RL/DRL in AIoT. Next, a comprehensive survey of the state-of-art research on DRL for AIoT is presented, where the existing works are classified and summarized under the umbrella of the proposed general DRL model. Finally, the challenges and open issues for future research are identified.
Understanding Adversarial Robustness Through Loss Landscape Geometries

The pursuit of explaining and improving generalization in deep learning has elicited efforts both in regularization techniques as well as visualization techniques of the loss surface geometry. The latter is related to the intuition prevalent in the community that flatter local optima leads to lower generalization error. In this paper, we harness the state-of-the-art "filter normalization" technique of loss-surface visualization to qualitatively understand the consequences of using adversarial training data augmentation as the explicit regularization technique of choice. Much to our surprise, we discover that this oft deployed adversarial augmentation technique does not actually result in "flatter" loss-landscapes, which requires rethinking adversarial training generalization, and the relationship between generalization and loss landscapes geometries.
A Competitive Analysis of Online Knapsack Problems with Unit Density

We study an online knapsack problem where the items arrive sequentially and must be either immediately packed into the knapsack or irrevocably discarded. Each item has a different size and the objective is to maximize the total size of items packed. While the competitive ratio of deterministic algorithms for this problem is known to be 0, the competitive ratio of randomized algorithms has, surprisingly, not been considered until now. We derive a random-threshold algorithm which is 0.432-competitive, and show that our threshold distribution is optimal. We also consider the generalization to multiple knapsacks, where an arriving item has a different size in each knapsack and must be placed in at most one. This is equivalent to the Adwords problem where item truncation is not allowed. We derive a randomized algorithm for this problem which is 0.214-competitive.
Multi-Class Lane Semantic Segmentation using Efficient Convolutional Networks

Lane detection plays an important role in a self-driving vehicle. Several studies leverage a semantic segmentation network to extract robust lane features, but few of them can distinguish different types of lanes. In this paper, we focus on the problem of multi-class lane semantic segmentation. Based on the observation that the lane is a small-size and narrow-width object in a road scene image, we propose two techniques, Feature Size Selection (FSS) and Degressive Dilation Block (DD Block). The FSS allows a network to extract thin lane features using appropriate feature sizes. To acquire fine-grained spatial information, the DD Block is made of a series of dilated convolutions with degressive dilation rates. Experimental results show that the proposed techniques provide obvious improvement in accuracy, while they achieve the same or faster inference speed compared to the baseline system, and can run at real-time on high-resolution images.
Geographically Coordinated Primary Frequency Control Technical Report.

Primary Frequency Control (PFC) is a fast acting mechanism used to ensure high-quality power for the grid that is becoming an increasingly attractive option for load participation. Due to speed requirement and other considerations, it is often desirable to have distributed control laws. Current distributed PFC designs assume that the costs at each geographic location are independent. However, many networked systems, such as those for cloud computing, have interdependent costs across locations and therefore need geographic coordination. In this paper, distributed control laws are designed for interdependent, geo-distributed loads in PFC based on the optimality conditions of the global system. The controlled frequencies are provably stable, and the final equilibrium point is proven to strike an optimal balance between load participation and the frequency's deviation from its nominal set point. The case with a significant communication delay for control is also analyzed. We evaluate the proposed control laws with realistic numerical simulations. Under current technology, the proposed control laws achieve a convergence time that is smaller than droop control alone and is comparable to that of distributed control without interdependent costs. Results also highlight significant cost savings over existing approaches under a variety of settings.
PH-GCN: Person Re-identification with Part-based Hierarchical Graph Convolutional Network

The person re-identification (Re-ID) task requires to robustly extract feature representations for person images. Recently, part-based representation models have been widely studied for extracting the more compact and robust feature representations for person images to improve person Re-ID results. However, existing part-based representation models mostly extract the features of different parts independently which ignore the relationship information between different parts. To overcome this limitation, in this paper we propose a novel deep learning framework, named Part-based Hierarchical Graph Convolutional Network (PH-GCN) for person Re-ID problem. Given a person image, PH-GCN first constructs a hierarchical graph to represent the pairwise relationships among different parts. Then, both local and global feature learning are performed by the messages passing in PH-GCN, which takes other nodes information into account for part feature representation. Finally, a perceptron layer is adopted for the final person part label prediction and re-identification. The proposed framework provides a general solution that integrates local, global and structural feature learning simultaneously in a unified end-to-end network. Extensive experiments on several benchmark datasets demonstrate the effectiveness of the proposed PH-GCN based Re-ID approach.
Sound Search by Text Description or Vocal Imitation?

Searching sounds by text labels is often difficult, as text descriptions cannot describe the audio content in detail. Query by vocal imitation bridges such gap and provides a novel way to sound search. Several algorithms for sound search by vocal imitation have been proposed and evaluated in a simulation environment, however, they have not been deployed into a real search engine nor evaluated by real users. This pilot work conducts a subjective study to compare these two approaches to sound search, and tries to answer the question of which approach works better for what kinds of sounds. To do so, we developed two web-based search engines for sound, one by vocal imitation (Vroom! ) and the other by text description (TextSearch). We also developed an experimental framework to host these engines to collect statistics of user behaviors and ratings. Results showed that Vroom! received significantly higher search satisfaction ratings than TextSearch did for sound categories that were difficult for subjects to describe by text. Results also showed a better overall ease-of-use rating for Vroom! than TextSearch on the limited sound library in our experiments. These findings suggest advantages of vocal-imitation-based search for sound in practice.