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Polarized attosecond $\gamma-$rays may offer excitation and hyperfine tracking of reactions relevant to nuclear physics, astrophysics, high-energy physics, etc. However, unfortunately, generation of a feasible and easy-to-deploy source is still a great challenge. Here, we put forward a novel method for producing ultra-collimated high-brilliance polarized attosecond $\gamma-$rays via the interaction of an unpolarized electron beam with a solid-density plasma. As a relativistic electron beam enters a solid-density plasma, it can be modulated into high-density clusters via the self-modulation instability of itself and further into attosecond slices due to its own hosing instability. This is accompanied by the generation of similar pulse-width $\gamma-$slices via nonlinear Compton scattering. The severe hosing instability breaks the symmetry of the excited electromagnetic fields, resulting in net linear polarization of $\gamma-$slices, which challenges the conventional perception that the interaction of an axially symmetric unpolarized electron beam with a uniform plasma cannot generate polarized radiation. In addition, we also obtain high-quality electron microbunches which may serve as an alternative source for prebunched free-electron lasers.

Signed graph neural networks (SGNNs) has recently drawn more attention as many real-world networks are signed networks containing two types of edges: positive and negative. The existence of negative edges affects the SGNN robustness on two aspects. One is the semantic imbalance as the negative edges are usually hard to obtain though they can provide potentially useful information. The other is the structural unbalance, e.g. unbalanced triangles, an indication of incompatible relationship among nodes. In this paper, we propose a balancing augmentation method to address the above two aspects for SGNNs. Firstly, the utility of each negative edge is measured by calculating its occurrence in unbalanced structures. Secondly, the original signed graph is selectively augmented with the use of (1) an edge perturbation regulator to balance the number of positive and negative edges and to determine the ratio of perturbed edges to original edges and (2) an edge utility filter to remove the negative edges with low utility to make the graph structure more balanced. Finally, a SGNN is trained on the augmented graph which effectively explores the credible relationships. A detailed theoretical analysis is also conducted to prove the effectiveness of each module. Experiments on five real-world datasets in link prediction demonstrate that our method has the advantages of effectiveness and generalization and can significantly improve the performance of SGNN backbones., Comment: 16 pages

We put forward a novel method for producing ultrarelativistic high-density high-polarization positrons through a single-shot interaction of a strong laser with a tilted solid foil. In our method, the driving laser ionizes the target, and the emitted electrons are accelerated and subsequently generate abundant $\gamma$ photons via the nonlinear Compton scattering, dominated by the laser. These $\gamma$ photons then generate polarized positrons via the nonlinear Breit-Wheeler process, dominated by a strong self-generated quasi-static magnetic field $\mathbf{B}^{\rm S}$. We find that placing the foil at an appropriate angle can result in a directional orientation of $\mathbf{B}^{\rm S}$, thereby polarizing positrons. Manipulating the laser polarization direction can control the angle between the $\gamma$ photon polarization and $\mathbf{B}^{\rm S}$, significantly enhancing the positron polarization degree. Our spin-resolved quantum electrodynamics particle-in-cell simulations demonstrate that employing a laser with a peak intensity of about $10^{23}$ W/cm$^2$ can obtain dense ($\gtrsim$ 10$^{18}$ cm$^{-3}$) polarized positrons with an average polarization degree of about 70\% and a yield of above 0.1 nC per shot. Moreover, our method is feasible using currently available or upcoming laser facilities and robust with respect to the laser and target parameters. Such high-density high-polarization positrons hold great significance in laboratory astrophysics, high-energy physics and new physics beyond the Standard Model.

We report the observations of FRB 20220912A using the Five-hundred-meter Aperture Spherical radio Telescope (FAST). We conducted 17 observations totaling 8.67 hours and detected a total of 1076 bursts with an event rate up to 390 hr$^{-1}$. The cumulative energy distribution can be well described using a broken power-law function with the lower and higher-energy slopes of $-0.38\pm0.02$ and $-2.07\pm0.07$, respectively. We also report the L band ($1-1.5$ GHz) spectral index of the synthetic spectrum of FRB~20220912A bursts, which is $-2.6\pm0.21$. The average rotation measure (RM) value of the bursts from FRB~20220912A is $-0.08\pm5.39\ \rm rad\,m^{-2}$, close to 0 $\rm rad\,m^{-2}$ and maintain relatively stable over two months. Most bursts have nearly 100\% linear polarization. About 45\% of the bursts have circular polarization with SNR $>$ 3, and the highest circular polarization degree can reach 70\%. Our observations suggest that FRB~20220912A is located in a relatively clean local environment with complex circular polarization characteristics. These various behaviors imply that the mechanism of circular polarization of FRBs likely originates from an intrinsic radiation mechanism, such as coherent curvature radiation or inverse Compton scattering inside the magnetosphere of the FRB engine source (e.g. a magnetar)., Comment: 15 pages, 11 figures, 2 tables, submitted to ApJ, comments are welcome!

We present the interstellar scintillation analysis of fast radio burst (FRB) 20220912A during its extremely active episode in 2022 using data from the Five-hundred-meter Aperture Spherical Radio Telescope (FAST). We detect a scintillation arc in the FRB's secondary spectrum, which describes the power in terms of the scattered FRB signals' time delay and Doppler shift. The arc indicates that the scintillation is caused by a highly localized region of the ionized interstellar medium (IISM). Our analysis favors a Milky Way origin for the localized scattering medium but cannot rule out a host galaxy origin. We present our method for detecting the scintillation arc, which can be applied generally to sources with irregularly spaced bursts or pulses. These methods could help shed light on the complex interstellar environment surrounding the FRBs and in our Galaxy., Comment: SCIENCE CHINA Physics, Mechanics & Astronomy , Volume 67, Issue 1: 219512 (2024)

Fast radio bursts (FRBs) are highly dispersed millisecond-duration radio bursts, of which the physical origin is still not fully understood. FRB 20201124A is one of the most actively repeating FRBs. In this paper, we present the collection of 1863 burst dynamic spectra of FRB 20201124A measured with the Five-hundred-meter Aperture Spherical radio Telescope (FAST). The current collection, taken from the observation during the FRB active phase from April to June 2021, is the largest burst sample detected in any FRB so far. The standard PSRFITs format is adopted, including dynamic spectra of the burst, and the time information of the dynamic spectra, in addition, mask files help readers to identify the pulse positions are also provided.

We report the properties of more than 800 bursts detected from the repeating fast radio burst (FRB) source FRB 20201124A with the Five-hundred-meter Aperture Spherical radio Telescope (FAST) during an extremely active episode on UTC September 25-28, 2021 in a series of four papers. In this second paper of the series, we mainly focus on the energy distribution of the detected bursts. The event rate initially increased exponentially but the source activity stopped within 24 hours after the 4th day. The detection of 542 bursts in one hour during the fourth day marked the highest event rate detected from one single FRB source so far. The bursts have complex structures in the time-frequency space. We find a double-peak distribution of the waiting time, which can be modeled with two log-normal functions peaking at 51.22 ms and 10.05 s, respectively. Compared with the emission from a previous active episode of the source detected with FAST, the second distribution peak time is smaller, suggesting that this peak is defined by the activity level of the source. We calculate the isotropic energy of the bursts using both a partial bandwidth and a full bandwidth and find that the energy distribution is not significantly changed. We find that an exponentially connected broken-power-law function can fit the cumulative burst energy distribution well, with the lower and higher-energy indices being $-1.22\pm0.01$ and $-4.27\pm0.23$, respectively. Assuming a radio radiative efficiency of $\eta_r = 10^{-4}$, the total isotropic energy of the bursts released during the four days when the source was active is already $3.9\times10^{46}$ erg, exceeding $\sim 23\%$ of the available magnetar dipolar magnetic energy. This challenges the magnetar models invoking an inefficient radio emission (e.g. synchrotron maser models)., Comment: 26 pages, 7 figures, accepted for publication in Research in Astronomy and Astrophysics

We report the properties of more than 800 bursts detected from the repeating fast radio burst (FRB) source FRB 20201124A with the Five-hundred-meter Aperture Spherical radio telescope (FAST) during an extremely active episode on UTC September 25th-28th, 2021 in a series of four papers. In this fourth paper of the series, we present a systematic search of the spin period and linear acceleration of the source object from both 996 individual pulse peaks and the dedispersed time series. No credible spin period was found from this data set. We rule out the presence of significant periodicity in the range between 1 ms to 100 s with a pulse duty cycle $< 0.49\pm0.08$ (when the profile is defined by a von-Mises function, not a boxcar function) and linear acceleration up to $300$ m s$^{-2}$ in each of the four one-hour observing sessions, and up to $0.6$ m s$^{-2}$ in all 4 days. These searches contest theoretical scenarios involving a 1 ms to 100 s isolated magnetar/pulsar with surface magnetic field $<10^{15}$ G and a small duty cycle (such as in a polar-cap emission mode) or a pulsar with a companion star or black hole up to 100 M$_{\rm \odot}$ and $P_b>10$ hours. We also perform a periodicity search of the fine structures and identify 53 unrelated millisecond-timescale "periods" in multi-components with the highest significance of 3.9 $\sigma$. The "periods" recovered from the fine structures are neither consistent nor harmonically related. Thus they are not likely to come from a spin period. We caution against claiming spin periodicity with significance below $\sim$ 4 $\sigma$ with multi-components from one-off FRBs. We discuss the implications of our results and the possible connections between FRB multi-components and pulsar micro-structures., Comment: Accepted by Research in Astronomy and Astrophysics (RAA)

As the third paper in the multiple-part series, we report the statistical properties of radio bursts detected from the repeating fast radio burst (FRB) source FRB 20201124A with the Five-hundred-meter Aperture Spherical radio telescope (FAST) during an extremely active episode between the 25th and the 28th of September 2021 (UT). We focus on the polarisation properties of 536 bright bursts with $\mathrm{S/N}>50$. We found that the Faraday rotation measures (RMs) monotonically dropped from $-579 \ {\rm rad \ m^{-2}}$ to $-605 \ {\rm rad \ m^{-2}}$ in the 4-day window. The RM values were compatible with the values ($-300$ to $-900\ {\rm rad \ m^{-2}}$ ) reported 4 month ago (Xu et al. 2022). However, the RM evolution rate in the current observation window was at least an order of magnitude smaller than the one ($\sim 500\ {\rm rad \ m^{-2}\, day^{-1}}$) previously reported during the rapid RM-variation phase, but is still higher than the one ($\le 1\ {\rm rad \ m^{-2} day^{-1}}$ ) during the later RM no-evolution phase. The bursts of FRB 20201124A were highly polarised with the total degree of polarisation (circular plus linear) greater than 90% for more than 90\% of all bursts. The distribution of linear polarisation position angles (PAs), degree of linear polarisation ($L/I$), and degree of circular polarisation ($V/I$) can be characterised with unimodal distribution functions. During the observation window, the distributions became wider with time, i.e. with larger scatter, but the centroids of the distribution functions remained nearly constant. For individual bursts, significant PA variations (confidence level 5-$\sigma$) were observed in 33% of all bursts. The polarisation of single pulses seems to follow certain complex trajectories on the Poincar\'e sphere, which may shed light on the radiation mechanism at the source or the plasma properties along the path of FRB propagation., Comment: 25 pages, 16 figures. Accepted by Research in Astronomy and Astrophysics (RAA)

FRB180301 is one of the most actively repeating fast radio bursts (FRBs) which has shown polarization angle changes in its radio burst emission, an indication for their likely origin in the magnetosphere of a highly-magnetized neutron star. We carried out a multi-wavelength campaign with the FAST radio telescope and NICER X-ray observatory to investigate any possible X-ray emission temporally coincident with the bright radio bursts. The observations took place on 2021 March 4, 9 and 19. We detected five bright radio bursts with FAST, four of which were strictly simultaneous with the NICER observations. The peak flux-density of the radio bursts ranged between $28-105$ mJy, the burst fluence between $27-170$ mJy-ms, and the burst durations between $1.7-12.3$ ms. The radio bursts from FRB~180301 exhibited complex time domain structure, and sub-pulses were detected in individual bursts, with no significant circular polarisation. The linear degree of polarisation in L-band reduced significantly compared to the 2019 observations. We do not detect any X-ray emission in excess of the background during the 5ms, 10ms, 100ms, 1sec and 100sec time intervals at/around the radio-burst barycenter-corrected arrival times, at a $>5\sigma$ confidence level. The $5\sigma$ upper limits on the X-ray a) persistent flux is $<7.64\times 10^{-12}\, \rm erg\, cm^{-2}\, s^{-1}$ , equivalent to $L_{\rm X}<2.50 \times 10^{45} \rm erg\, s^{-1}$ and b) 5 ms fluence is $<2\times 10^{-11} \rm erg\, cm^{-2}$, at the radio burst regions. Using the $5$ ms X-ray fluence upper limit, we can estimate the radio efficiency $\eta_{R/X} \equiv L_{\rm Radio}/L_{\rm X-ray} \gtrsim 10^{-8}$. The derived upper limit on $\eta_{R/X}$ is consistent with both magnetospheric models and synchrotron maser models involving relativistic shocks., Comment: Resubmitted to ApJ following referee's minor comments

Self-supervised learning on graphs has recently drawn a lot of attention due to its independence from labels and its robustness in representation. Current studies on this topic mainly use static information such as graph structures but cannot well capture dynamic information such as timestamps of edges. Realistic graphs are often dynamic, which means the interaction between nodes occurs at a specific time. This paper proposes a self-supervised dynamic graph representation learning framework (DySubC), which defines a temporal subgraph contrastive learning task to simultaneously learn the structural and evolutional features of a dynamic graph. Specifically, a novel temporal subgraph sampling strategy is firstly proposed, which takes each node of the dynamic graph as the central node and uses both neighborhood structures and edge timestamps to sample the corresponding temporal subgraph. The subgraph representation function is then designed according to the influence of neighborhood nodes on the central node after encoding the nodes in each subgraph. Finally, the structural and temporal contrastive loss are defined to maximize the mutual information between node representation and temporal subgraph representation. Experiments on five real-world datasets demonstrate that (1) DySubC performs better than the related baselines including two graph contrastive learning models and four dynamic graph representation learning models in the downstream link prediction task, and (2) the use of temporal information can not only sample more effective subgraphs, but also learn better representation by temporal contrastive loss.

Sequential recommendation based on multi-interest framework models the user's recent interaction sequence into multiple different interest vectors, since a single low-dimensional vector cannot fully represent the diversity of user interests. However, most existing models only intercept users' recent interaction behaviors as training data, discarding a large amount of historical interaction sequences. This may raise two issues. On the one hand, data reflecting multiple interests of users is missing; on the other hand, the co-occurrence between items in historical user-item interactions is not fully explored. To tackle the two issues, this paper proposes a novel sequential recommendation model called "Global Interaction Aware Multi-Interest Framework for Sequential Recommendation (GIMIRec)". Specifically, a global context extraction module is firstly proposed without introducing any external information, which calculates a weighted co-occurrence matrix based on the constrained co-occurrence number of each item pair and their time interval from the historical interaction sequences of all users and then obtains the global context embedding of each item by using a simplified graph convolution. Secondly, the time interval of each item pair in the recent interaction sequence of each user is captured and combined with the global context item embedding to get the personalized item embedding. Finally, a self-attention based multi-interest framework is applied to learn the diverse interests of users for sequential recommendation. Extensive experiments on the three real-world datasets of Amazon-Books, Taobao-Buy and Amazon-Hybrid show that the performance of GIMIRec on the Recall, NDCG and Hit Rate indicators is significantly superior to that of the state-of-the-art methods. Moreover, the proposed global context extraction module can be easily transplanted to most sequential recommendation models.

The pre-training on the graph neural network model can learn the general features of large-scale networks or networks of the same type by self-supervised methods, which allows the model to work even when node labels are missing. However, the existing pre-training methods do not take network evolution into consideration. This paper proposes a pre-training method on dynamic graph neural networks (PT-DGNN), which uses dynamic attributed graph generation tasks to simultaneously learn the structure, semantics, and evolution features of the graph. The method includes two steps: 1) dynamic sub-graph sampling, and 2) pre-training with dynamic attributed graph generation task. Comparative experiments on three realistic dynamic network datasets show that the proposed method achieves the best results on the link prediction fine-tuning task.

We study the pair production of charged scalar particles from the five-dimensional near extremal Reissner- Nordstr\"om-Anti de Sitter (RN-AdS5) black hole. The pair production rate and the absorption cross section ratio in the full spacetime are obtained and are shown to have proportional relation with their counterparts in the near horizon region. In addition, the holographic descriptions of the pair production both in the IR CFT in the near horizon region and the UV CFT at the asymptotic spatial boundary of the RN-AdS5 black hole are analyzed in the AdS2/CFT1and AdS5/CFT4correspondences, respectively. This work gives a complete description of scalar pair production in the near extremal RN-AdS5black hole., Comment: 23pages, no figures

In this paper, we consider sequential online prediction (SOP) for streaming data in the presence of outliers and change points. We propose an INstant TEmporal structure Learning (INTEL) algorithm to address this problem. Our INTEL algorithm is developed based on a full consideration of the duality between online prediction and anomaly detection. We first employ a mixture of weighted Gaussian process models (WGPs) to cover the expected possible temporal structures of the data. Then, based on the rich modeling capacity of this WGP mixture, we develop an efficient technique to instantly learn (capture) the temporal structure of the data that follows a regime shift. This instant learning is achieved only by adjusting one hyper-parameter value of the mixture model. A weighted generalization of the product of experts (POE) model is used for fusing predictions yielded from multiple GP models. An outlier is declared once a real observation seriously deviates from the fused prediction. If a certain number of outliers are consecutively declared, then a change point is declared. Extensive experiments are performed using a diverse of real datasets. Results show that the proposed algorithm is significantly better than benchmark methods for SOP in the presence of outliers and change points., Comment: 43 pages, 14 figures

In magnetic topological insulators (TIs), the interplay between magnetic order and nontrivial topology can induce fascinating topological quantum phenomena, such as the quantum anomalous Hall effect, chiral Majorana fermions and axion electrodynamics. Recently, a great deal of attention has been focused on the intrinsic magnetic TIs, where disorder effects can be eliminated to a large extent, which is expected to facilitate the emergence of topological quantum phenomena. In despite of intensive efforts, experimental evidence of the topological surface states (SSs) remains elusive. Here, by combining first-principles calculations and angle-resolved photoemission spectroscopy (ARPES) experiments, we have revealed that EuSn2As2 is an antiferromagnetic TI with observation of Dirac SSs consistent with our prediction. We also observe nearly gapless Dirac SSs in antiferromagnetic TIs MnBi2nTe3n+1 (n = 1 and 2), which were absent in previous ARPES results. These results provide clear evidence for nontrivial topology of these intrinsic magnetic TIs. Furthermore, we find that the topological SSs show no observable changes across the magnetic transition within the experimental resolution, indicating that the magnetic order has quite small effect on the topological SSs, which can be attributed to weak hybridization between the localized magnetic moments, from either 4f or 3d orbitals, and the topological electronic states. This provides insights for further research that the correlations between magnetism and topological states need to be strengthened to induce larger gaps in the topological SSs, which will facilitate the realization of topological quantum phenomena at higher temperatures., Comment: 22 pages, 5 figures, to be published in PRX

The development of long range order in nickel-chromium alloys is of great technological interest but the kinetics and mechanisms of the transformation are poorly understood. The present research utilizes a combined computational and experimental approach to elucidate the mechanism by which phosphorus accelerates the ordering rate of stoichiometric Ni_2Cr in Ni-Cr alloys. A series of Ni-33%Cr-x%P samples (in atomic percent) were fabricated with phosphorus concentrations, x = <0.005-0.1 at.% and aged between 373 and 470{\deg}C for times up to 3000 h. The first-principles modeling considers fcc Ni with dilute P as a reasonable approximation for the complex Ni-Cr-P alloy system. Calculation results show a pronounced enhancement of vacancy transport by vacancy-solute pair diffusion via consecutive exchange and rotation jumps of vacancies associated with the phosphorus atom. The energy barriers of these two migration paths are at least 0.35 eV lower than that of vacancy-atom exchange in pure Ni solvent. The analytical diffusion model predicts enhanced solvent diffusion by 2 orders of magnitude for 0.1 at.% P at 400-500{\deg}C. The model prediction is in good agreement with the evolution of micro-hardness. We characterize the micro-hardness result by a kinetic ordering model, showing a significant decrease of the activation energy of ordering transformation. These results help gauge the risk of industrial alloys developing long range order which increases strength but degrades ductility and toughness. Specifically, minor alloying additions that bind with excess vacancies and lower the vacancy migration barrier can greatly accelerate hardening via Ni_2Cr precipitation.

Radiation-enhanced precipitation of Cr-rich {\alpha}' in irradiated Fe-Cr alloys, which results in hardening and embrittlement, depends on the irradiating particle and the displacement per atom (dpa) rate. Here, we utilize a Cahn-Hilliard phase-field based approach, that includes simple models for nucleation, irradiating particle and rate dependent radiation-enhanced diffusion and cascade mixing to simulate {\alpha}' evolution under neutrons, heavy ions, and electron irradiations. Different irradiating particles manifest very different cascade mixing efficiencies. The model was calibrated using neutron data. For cascade inducing neutron/heavy-ion dpa rates at 300 {\deg}C between 10^-^8 and 10^-^6 dpa/s the model predicts approximately constant number density, decreasing radius, decreasing {\alpha}' Cr composition, and lower {\alpha}' volume fraction. The model then predicts a dramatic transition to no {\alpha}' formation above approximately 10^-^5 dpa/s, while electron irradiation, with weak mixing, had little effect at dpa rates up to 10^-^3 dpa/s. These model predictions are consistent with experiments. We explain the results in terms of the flux dependence of the radiation enhanced diffusion, cascade mixing, and their ratio, which all vary significantly in relevant flux ranges for neutron and cascade inducing ion irradiations. These results show that both cascade mixing and radiation enhanced diffusion must be accounted for when attempting to emulate neutron-irradiation effects using accelerated ion irradiations.

We present integrated pulse profiles at 5~GHz for 71 pulsars, including eight millisecond pulsars (MSPs), obtained using the Shanghai Tian Ma Radio Telescope (TMRT). Mean flux densities and pulse widths are measured. For 19 normal pulsars and one MSP, these are the first detections at 5~GHz and for a further 19, including five MPSs, the profiles have a better signal-to-noise ratio than previous observations. Mean flux density spectra between 400~MHz and 9~GHz are presented for 27 pulsars and correlations of power-law spectral index are found with characteristic age, radio pseudo-luminosity and spin-down luminosity. Mode changing was detected in five pulsars. The separation between the main pulse and interpulse is shown to be frequency independent for six pulsars but a frequency dependence of the relative intensity of the main pulse and interpulse is found. The frequency dependence of component separations is investigated for 20 pulsars and three groups are found: in seven cases the separation between the outmost leading and trailing components decreases with frequency, roughly in agreement with radius-to-frequency mapping; in eleven cases the separation is nearly constant; in the remain two cases the separation between the outmost components increases with frequency. We obtain the correlations of pulse widths with pulsar period and estimate the core widths of 23 multi-component profiles and conal widths of 17 multi-component profiles at 5.0~GHz using Gaussian fitting and discuss the width-period relationship at 5~GHz compared with the results at at 1.0~GHz and 8.6~GHz., Comment: 46 pages, 14 figures, 8 Tables, accepted by ApJ

Mn-Ni-Si precipitates (MNSPs) are known to be responsible for irradiation-induced hardening and embrittlement in structural alloys used in nuclear reactors. Studies have shown that precipitation of the MNSPs in 9-Cr ferritic-martensitic (F-M) alloys, such as T91, is strongly associated with heterogeneous nucleation on dislocations, coupled with radiation-induced solute segregation to these sinks. Therefore it is important to develop advanced predictive models for Mn-Ni-Si precipitation in F-M alloys under irradiation based on an understanding of the underlying mechanisms. Here we use a cluster dynamics model, which includes multiple effects of dislocations, to study the evolution of MNSPs in a commercial F-M alloy T91. The model predictions are calibrated by data from proton irradiation experiments at 400 {\deg}C. Radiation induced solute segregation at dislocations is evaluated by a continuum model that is integrated into the cluster dynamics simulations, including the effects of dislocations as heterogeneous nucleation sites. The result shows that MNSPs in T91 are primarily irradiation-induced and, in particular, both heterogeneous nucleation and radiation-induced segregation at dislocations are necessary to rationalize the experimental observations.

The growth of Internet commerce has stimulated the use of collaborative filtering (CF) algorithms as recommender systems. A collaborative filtering (CF) algorithm recommends items of interest to the target user by leveraging the votes given by other similar users. In a standard CF framework, it is assumed that the credibility of every voting user is exactly the same with respect to the target user. This assumption is not satisfied and thus may lead to misleading recommendations in many practical applications. A natural countermeasure is to design a trust-aware CF (TaCF) algorithm, which can take account of the difference in the credibilities of the voting users when performing CF. To this end, this paper presents a trust inference approach, which can predict the implicit trust of the target user on every voting user from a sparse explicit trust matrix. Then an improved CF algorithm termed iTrace is proposed, which takes advantage of both the explicit and the predicted implicit trust to provide recommendations with the CF framework. An empirical evaluation on a public dataset demonstrates that the proposed algorithm provides a significant improvement in recommendation quality in terms of mean absolute error (MAE)., Comment: 6 pages, 4 figures, 1 table

Integrated pulse profiles at 8.6~GHz obtained with the Shanghai Tian Ma Radio Telescope (TMRT) are presented for a sample of 26 pulsars. Mean flux densities and pulse width parameters of these pulsars are estimated. For eleven pulsars these are the first high-frequency observations and for a further four, our observations have a better signal-to-noise ratio than previous observations. For one (PSR J0742-2822) the 8.6~GHz profiles differs from previously observed profiles. A comparison of 19 profiles with those at other frequencies shows that in nine cases the separation between the outmost leading and trailing components decreases with frequency, roughly in agreement with radius-to-frequency mapping, whereas in the other ten the separation is nearly constant. Different spectral indices of profile components lead to the variation of integrated pulse profile shapes with frequency. In seven pulsars with multi-component profiles, the spectral indices of the central components are steeper than those of the outer components. For the 12 pulsars with multi-component profiles in the high-frequency sample, we estimate the core width using gaussian fitting and discuss the width-period relationship., Comment: 33 pages, 49 figures, 5 Tables; accepted by ApJ

This paper addresses maximum likelihood (ML) estimation based model fitting in the context of extrasolar planet detection. This problem is featured by the following properties: 1) the candidate models under consideration are highly nonlinear; 2) the likelihood surface has a huge number of peaks; 3) the parameter space ranges in size from a few to dozens of dimensions. These properties make the ML search a very challenging problem, as it lacks any analytical or gradient based searching solution to explore the parameter space. A population based searching method, called estimation of distribution algorithm (EDA), is adopted to explore the model parameter space starting from a batch of random locations. EDA is featured by its ability to reveal and utilize problem structures. This property is desirable for characterizing the detections. However, it is well recognized that EDAs can not scale well to large scale problems, as it consists of iterative random sampling and model fitting procedures, which results in the well-known dilemma curse of dimensionality. A novel mechanism to perform EDAs in interactive random subspaces spanned by correlated variables is proposed and the hope is to alleviate the curse of dimensionality for EDAs by performing the operations of sampling and model fitting in lower dimensional subspaces. The effectiveness of the proposed algorithm is verified via both benchmark numerical studies and real data analysis., Comment: 12 pages, 5 figures, conference

The pulse profile of PSR B1133+16 is usually regarded as a conal-double structure. However, its multifrequency profiles cannot simply be fitted with two Gaussian functions, and a third component is always needed to fit the bridge region (between two peaks). This would introduce additional, redundant parameters. In this paper, through a comparison of five fitting functions (Gaussian, von Mises, hyperbolic secant, square hyperbolic secant, and Lorentz), it is found that the square hyperbolic secant function can best reproduce the profile, yielding an improved fit. Moreover, a symmetric 2D radiation beam function, instead of a simple 1D Gaussian function, is used to fit the profile. Each profile with either well-resolved or not-so-well-resolved peaks could be fitted adequately using this beam function, and the bridge emission between the two peaks does not need to be a new component. Adopting inclination and impact angles based on polarization measurements, the opening angle ({\theta}_{\mu}0) of the radiation beam in a certain frequency band is derived from beam-function fitting. The corresponding radiation altitudes are then calculated. Based on multi-frequency profiles, we also computed the Lorentz factors of the particles and their dispersion at those locations in both the curvature-radiation (CR) and inverse-Compton-scattering (ICS) models. We found that the Lorentz factors of the particles decrease rapidly as the radiation altitude increases. Besides, the radiation prefers to be generated in annular region rather than core region, and this needs further validation., Comment: 13 pages, 18 figures, ApJ, 816, 76

In this paper, we report radio observations of the Galactic Center magnetar PSR J1745-2900 at six epochs between June and October, 2014. These observations were carried out using the new Shanghai Tian Ma Radio Telescope at a frequency of 8.6 GHz. Both the flux density and integrated profile of PSR J1745-2900 show dramatic changes from epoch to epoch showing that the pulsar was in its "erratic" phase. On MJD 56836, the flux density of this magnetar was about 8.7 mJy, which was ten times large than that reported at the time of discovery, enabling a single-pulse analysis. The emission is dominated by narrow "spiky" pulses which follow a log-normal distribution in peak flux density. From 1913 pulses, we detected 53 pulses whose peak flux density is ten times greater than that of the integrated profile. They are concentrated in pulse phase at the peaks of the integrated profile. The pulse widths at the 50% level of these bright pulses was between 0.2 to 0.9 deg, much narrower than that of integrated profile (~12 deg). The observed pulse widths may be limited by interstellar scattering. No clear correlation was found between the widths and peak flux density of these pulses and no evidence was found for subpulse drifting. Relatively strong spiky pulses are also detected in the other five epochs of observation, showing the same properties as that detected in MJD 56836. These strong spiky pulses cannot be classified as "giant" pulses but are more closely related to normal pulse emission., Comment: 21 papes, 7 figures, accepted by ApJ

In this paper, we numerically study quantum walks on two kinds of two-dimensional graphs: cylindrical strip and Mobius strip. The two kinds of graphs are typical two-dimensional topological graph. We study the crossing property of quantum walks on these two models. Also, we study its dependence on the initial state, size of the model. At the same time, we compare the quantum walk and classical walk on these two models to discuss the difference of quantum walk and classical walk.

Lazy quantum walks were presented by Andrew M. Childs to prove that the continuous-time quantum walk is a limit of the discrete-time quantum walk [Commun.Math.Phys.294,581-603(2010)]. In this paper, we discuss properties of lazy quantum walks. Our analysis shows that lazy quantum walks have $O(t^n)$ order of the n-th moment of the corresponding probability distribution, which is the same as that for normal quantum walks. Also, the lazy quantum walk with DFT (Discrete Fourier Transform) coin operator has a similar probability distribution concentrated interval to that of the normal Hadamard quantum walk. Most importantly, we introduce the concepts of occupancy number and occupancy rate to measure the extent to which the walk has a (relatively) high probability at every position in its range. We conclude that lazy quantum walks have a higher occupancy rate than other walks such as normal quantum walks, classical walks and lazy classical walks.