The frequency-dependent periodic active window of the fast radio burst FRB 180916.J0158+65 (FRB 180916B) was observed recently. In this Letter, we propose that a Be/X-ray binary (BeXRB) system, which is composed of a neutron star (NS) and a Be star w
ith a circumstellar disk, might be the source of a repeating FRB with periodic activities, and apply this model to explain the activity window of FRB 180916B. The interaction between the NS magnetosphere and the accreted material results in evolution of the spin period and the centrifugal force of the NS, leading to the change of the stress in the NS crust. When the stress of the crust reaches the critical value, a starquake occurs and further produces FRBs. The interval between starquakes is estimated to be a few days that is smaller than the active window of FRB 180916B. When the NS moves out of the disk of the Be star, the interval between starquakes becomes much longer than the orbital period, which corresponds to the non-active phase. In this model, due to the absorption of the disk of the Be star, a frequency-dependent active window would appear for the FRBs, which is consistent with the observed properties of FRB 180916B. And the contribution of dispersion measure (DM) from the disk of the Be star is small. In addition, the location of FRB 180916B in the host galaxy is consistent with a BeXRB system.
In this work, we study the impact of the multiplicative phase noise in an IRS-assisted system. We consider an IRS-assisted system with multiplicative phase noise both at the BS and user. A novel channel estimation algorithm is proposed considering th
e phase noise. By utilizing the proposed channel estimates we investigate the system performance in the downlink, more specifically, we derive the ergodic capacity in closed form. Simulation results verify the correctness of the closed-form expression. We observe that the system becomes more robust against the phase noise as the number of reflective elements increases. Moreover, the influence of the additive channel noise in uplink vanishes as the number of reflecting elements grows asymptotically large.
Biological screens are plagued by false positive hits resulting from aggregation. Thus, methods to triage small colloidally aggregating molecules (SCAMs) are in high demand. Herein, we disclose a bespoke machine-learning tool to confidently and intel
ligibly flag such entities. Our data demonstrate an unprecedented utility of machine learning for predicting SCAMs, achieving 80% of correct predictions in a challenging out-of-sample validation. The tool outperformed a panel of expert chemists, who correctly predicted 61 +/- 7% of the same test molecules in a Turing-like test. Further, the computational routine provided insight into molecular features governing aggregation that had remained hidden to expert intuition. Leveraging our tool, we quantify that up to 15-20% of ligands in publicly available chemogenomic databases have the high potential to aggregate at typical screening concentrations, imposing caution in systems biology and drug design programs. Our approach provides a means to augment human intuition, mitigate attrition and a pathway to accelerate future molecular medicine.
In this paper, we propose a novel navigation system for mobile robots in pedestrian-rich sidewalk environments. Sidewalks are unique in that the pedestrian-shared space has characteristics of both roads and indoor spaces. Like vehicles on roads, pede
strian movement often manifests as linear flows in opposing directions. On the other hand, pedestrians also form crowds and can exhibit much more random movements than vehicles. Classical algorithms are insufficient for safe navigation around pedestrians and remaining on the sidewalk space. Thus, our approach takes advantage of natural human motion to allow a robot to adapt to sidewalk navigation in a safe and socially-compliant manner. We developed a textit{group surfing} method which aims to imitate the optimal pedestrian group for bringing the robot closer to its goal. For pedestrian-sparse environments, we propose a sidewalk edge detection and following method. Underlying these two navigation methods, the collision avoidance scheme is human-aware. The integrated navigation stack is evaluated and demonstrated in simulation. A hardware demonstration is also presented.
Terrestrial laser scanning technology provides an efficient and accuracy solution for acquiring three-dimensional information of plants. The leaf-wood classification of plant point cloud data is a fundamental step for some forestry and biological res
earch. An automatic sampling and training method for classification was proposed based on tree point cloud data. The plane fitting method was used for selecting leaf sample points and wood sample points automatically, then two local features were calculated for training and classification by using support vector machine (SVM) algorithm. The point cloud data of ten trees were tested by using the proposed method and a manual selection method. The average correct classification rate and kappa coefficient are 0.9305 and 0.7904, respectively. The results show that the proposed method had better efficiency and accuracy comparing to the manual selection method.
We use the technique of information relaxation to develop a duality-driven iterative approach to obtaining and improving confidence interval estimates for the true value of finite-horizon stochastic dynamic programming problems. We show that the sequ
ence of dual value estimates yielded from the proposed approach in principle monotonically converges to the true value function in a finite number of dual iterations. Aiming to overcome the curse of dimensionality in various applications, we also introduce a regression-based Monte Carlo algorithm for implementation. The new approach can be used not only to assess the quality of heuristic policies, but also to improve them if we find that their duality gap is large. We obtain the convergence rate of our Monte Carlo method in terms of the amounts of both basis functions and the sampled states. Finally, we demonstrate the effectiveness of our method in an optimal order execution problem with market friction and in an inventory management problem in the presence of lost sale and lead time. Both examples are well known in the literature to be difficult to solve for optimality. The experiments show that our method can significantly improve the heuristics suggested in the literature and obtain new policies with a satisfactory performance guarantee.
The first repeating fast radio burst (FRB), FRB 121102, was found to be associated with a spatially coincident, persistent nonthermal radio source, but the origin of the persistent emission remains unknown. In this paper, we propose that the persiste
nt emission is produced via synchrotron-heating process by multiple bursts of FRB 121102 in a self-absorbed synchrotron nebula. As a population of bursts of the repeating FRB absorbed by the synchrotron nebula, the energy distribution of electrons in the nebula will change significantly. As a result, the spectrum of the nebula will show a hump steadily. For the persistent emission of FRB 121102, the total energy of bursts injecting into the nebula is required to be about $3.3times10^{49},unit{erg}$, the burst injection age is over $6.7times 10^4,unit{yr}$, the nebula size is $sim0.02,unit{pc}$, and the electron number is about $3.2times10^{55}$. We predict that as more bursts inject, the brightness of the nebula would be brighter than the current observation, and meanwhile, the peak frequency would become higher. Due to the synchrotron absorption of the nebula, some low-frequency bursts would be absorbed, which may explain why most bursts were detected above $sim1~unit{GHz}$.
The accurate classification of plant organs is a key step in monitoring the growing status and physiology of plants. A classification method was proposed to classify the leaves and stems of potted plants automatically based on the point cloud data of
the plants, which is a nondestructive acquisition. The leaf point training samples were automatically extracted by using the three-dimensional convex hull algorithm, while stem point training samples were extracted by using the point density of a two-dimensional projection. The two training sets were used to classify all the points into leaf points and stem points by utilizing the support vector machine (SVM) algorithm. The proposed method was tested by using the point cloud data of three potted plants and compared with two other methods, which showed that the proposed method can classify leaf and stem points accurately and efficiently.
The spreading of the X-line out of the reconnection plane under a strong guide field is investigated using large-scale three-dimensional (3D) particle-in-cell (PIC) simulations in asymmetric magnetic reconnection. A simulation with a thick, ion-scale
equilibrium current sheet (CS) reveals that the X-line spreads at the ambient ion/electron drift speeds, significantly slower than the Alfven speed based on the guide field $V_{Ag}$. Additional simulations with a thinner, sub-ion-scale CS show that the X-line spreads at $V_{Ag}$ (Alfvenic spreading), much higher than the ambient species drifts. An Alfvenic signal consistent with kinetic Alfven waves develops and propagates, leading to CS thinning and extending, which then results in reconnection onset. The continuous onset of reconnection in the signal propagation direction manifests as Alfvenic X-line spreading. The strong dependence on the CS thickness of the spreading speeds, and the X-line orientation are consistent with the collisionless tearing instability. Our simulations indicate that when the collisionless tearing growth is sufficiently strong in a thinner CS such that $gamma/Omega_{ci}gtrsimmathcal{O}(1)$, Alfvenic X-line spreading can take place. Our results compare favorably with a number of numerical simulations and recent magnetopause observations. A key implications is that the magnetopause CS is typically too thick for Alfvenic X-line spreading to effectively take place.
Turbulence is ubiquitously observed in nearly collisionless heliospheric plasmas, including the solar wind and corona and the Earths magnetosphere. Understanding the collisionless mechanisms responsible for the energy transfer from the turbulent fluc
tuations to the particles is a frontier in kinetic turbulence research. Collisionless energy transfer from the turbulence to the particles can take place reversibly, resulting in non-thermal energy in the particle velocity distribution functions (VDFs) before eventual collisional thermalization is realized. Exploiting the information contained in the fluctuations in the VDFs is valuable. Here we apply a recently developed method based on VDFs, the field-particle correlation technique, to a $beta=1$, solar-wind-like, low-frequency Alfvenic turbulence simulation with well resolved phase space to identify the field-particle energy transfer in velocity space. The field-particle correlations reveal that the energy transfer, mediated by the parallel electric field, results in significant structuring of the ion and electron VDFs in the direction parallel to the magnetic field. Fourier modes representing the length scales between the ion and electron gyroradii show that energy transfer is resonant in nature, localized in velocity space to the Landau resonances for each Fourier mode. The energy transfer closely follows the Landau resonant velocities with varying perpendicular wavenumber $k_perp$ and plasma $beta$. This resonant signature, consistent with Landau damping, is observed in all diagnosed Fourier modes that cover the dissipation range of the simulation.
