Automatic Differentiable Monte Carlo: Theory and Application

Differentiable programming has emerged as a key programming paradigm empowering rapid developments of deep learning while its applications to important computational methods such as Monte Carlo remain largely unexplored. Here we present the general theory enabling infinite-order automatic differentiation on expectations computed by Monte Carlo with unnormalized probability distributions, which we call "automatic differentiable Monte Carlo" (ADMC). By implementing ADMC algorithms on computational graphs, one can also leverage state-of-the-art machine learning frameworks and techniques to traditional Monte Carlo applications in statistics and physics. We illustrate the versatility of ADMC by showing some applications: fast search of phase transitions and accurately finding ground states of interacting many-body models in two dimensions. ADMC paves a promising way to innovate Monte Carlo in various aspects to achieve higher accuracy and efficiency, e.g. easing or solving the sign problem of quantum many-body models through ADMC.Comment: 11.5 pages + supplemental materials, 4 figure

A Foldable Tightly Coupled Crossed Rings Antenna Array of Ultrawide Bandwidth and Dual Polarization

Low-profile foldable array antennas are becoming increasingly more important for a wide range of applications such as satellite communications and wearable electronic devices. The conventional arrays formed by patch-like antennas have been extensively studied on surfaces with a curvature but they have exhibited limited bandwidth and polarization performance. This study investigates a coupling enhanced crossed rings antenna array with two typical configurations for dual polarization, which inherently produces ultrawide bandwidth, dipole-like polarization characteristics and a fully curved array (FCA) eventually. The fractional bandwidth of the array is over 100% on a planar surface and expanded to approximately 140% on the curved surface. For the bent array of slant polarization, the beamwidth increases by over 20° compared to the planar array and cross polarization discrimination (XPD) maintains above 15 dB. The effects of curvature on the impedance matching and polarized radiation patterns for such arrays are investigated by measuring the performance of the fabricated prototype arrays. The results revealed that the tightly coupled crossed rings antenna array on a curved surface has a potential to form multiple beams on a limited aperture size through smaller subarrays which can yield ultrawide bandwidth due to concentrated mutual coupling mechanism. This characteristic is promising in applications where traditional flat panel arrays are difficult to implement such as in mobile stations, moving platforms and for satellite communication on-the-move

A representation learning model based on variational inference and graph autoencoder for predicting lncRNA‑disease associations

Background: Numerous studies have demonstrated that long non-coding RNAs are related to plenty of human diseases. Therefore, it is crucial to predict potential lncRNAdisease associations for disease prognosis, diagnosis and therapy. Dozens of machine learning and deep learning algorithms have been adopted to this problem, yet it is still challenging to learn efficient low-dimensional representations from high-dimensional features of lncRNAs and diseases to predict unknown lncRNA-disease associations accurately. Results: We proposed an end-to-end model, VGAELDA, which integrates variational inference and graph autoencoders for lncRNA-disease associations prediction. VGAELDA contains two kinds of graph autoencoders. Variational graph autoencoders (VGAE) infer representations from features of lncRNAs and diseases respectively, while graph autoencoders propagate labels via known lncRNA-disease associations. These two kinds of autoencoders are trained alternately by adopting variational expectation maximization algorithm. The integration of both the VGAE for graph representation learning, and the alternate training via variational inference, strengthens the capability of VGAELDA to capture efficient low-dimensional representations from high-dimensional features, and hence promotes the robustness and preciseness for predicting unknown lncRNA-disease associations. Further analysis illuminates that the designed co-training framework of lncRNA and disease for VGAELDA solves a geometric matrix completion problem for capturing efficient low-dimensional representations via a deep learning approach. Conclusion: Cross validations and numerical experiments illustrate that VGAELDA outperforms the current state-of-the-art methods in lncRNA-disease association prediction. Case studies indicate that VGAELDA is capable of detecting potential lncRNAdisease associations. The source code and data are available at https:// github. com/ zhang labNKU/ VGAEL DA

A Dual Slant-Polarized Cylindrical Array of Tightly Coupled Dipole Antennas

This study proposes a design of a low-profile ultra wide-band cylindrical antenna array with plus/minus 45-degree dual polarization. The proposed compact cylindrical antenna array produces an omnidirectional radiation pattern in the azimuth plane to cover all directions. It consists of 20×4 dual-polarized elements within a diameter of 131 mm and a height of 116 mm. The array elements are tightly coupled slant-polarized wideband dipole antennas, and hence, rotational symmetry of radiation patterns in the horizontal plane is achieved for the two orthogonal polarizations. Furthermore, a metasurface structure has been designed and placed over the interconnected array elements to achieve ultrawideband capabilities. The proposed array provides less than −10 dB reflection coefficient over a frequency band between 1.7 GHz and 5.9 GHz. The cross-polarization discrimination (XPD) is 15 dB at boresight in the azimuth plane. The electromagnetic characteristics of the cylindrical array and its corresponding planar array before bending have been evaluated and compared via simulations, and verified by measurements. The compact size, lightweight, and printable design of the proposed antenna array enable low-cost manufacturing and ease of installation. The proposed array design overcomes many challenges encountered in wide-band MIMO systems by covering the entire sub-6 GHz band while providing wide 360-degree coverage in the azimuth plane, hence, supporting multibeam applications