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For approximate inference in the generalized quadratic equations model, many state-of-the-art algorithms lack any prior knowledge of the target signal structure, exhibits slow convergence, and can not handle any analytic prior knowledge of the target signal structure. So, this paper proposes a new algorithm, Quadratic Message passing (QMP). QMP has a complexity as low as $O(N^{3})$. The SE derived for QMP can capture precisely the per-iteration behavior of the simulated algorithm. Simulation results confirm QMP outperforms many state-of-the-art algorithms.

In this paper, we study the near-field radiative energy, linear-momentum, and angular-momentum transfer from a current-biased graphene to nanoparticles. The electric current through the graphene sheet induces nonequilibrium fluctuations, causing energy and momentum transfer even in the absence of a temperature difference. The inherent spin-momentum locking of graphene surface plasmons leads to an in-plane torque perpendicular to the direction of the electric current. In the presence of a temperature difference, the energy transfer is greatly enhanced while the lateral force and torque remain within the same order. Our work explores the potential of utilizing current-biased graphene to manipulate nanoparticles., Comment: 7 pages, 4 figures

For approximate inference in high-dimensional generalized linear models (GLMs), the performance of an estimator may significantly degrade when mismatch exists between the postulated model and the ground truth. In mismatched GLMs with rotation-invariant measurement matrices, Kabashima et al. proved vector approximate message passing (VAMP) computes exactly the optimal estimator if the replica symmetry (RS) ansatz is valid, but it becomes inappropriate if RS breaking (RSB) appears. Although the one-step RSB (1RSB) saddle point equations were given for the optimal estimator, the question remains: how to achieve the 1RSB prediction? This paper answers the question by proposing a new algorithm, vector approximate survey propagation (VASP). VASP derives from a reformulation of Kabashima's extremum conditions, which later links the theoretical equations to survey propagation in vector form and finally the algorithm. VASP has a complexity as low as VAMP, while embracing VAMP as a special case. The SE derived for VASP can capture precisely the per-iteration behavior of the simulated algorithm, and the SE's fixed point equations perfectly match Kabashima's 1RSB prediction, which indicates VASP can achieve the optimal performance even in a model-mismatched setting with 1RSB. Simulation results confirm VASP outperforms many state-of-the-art algorithms.

This paper explicitly models a coarse and noisy quantization in a communication system empowered by orthogonal time frequency space (OTFS) for cost and power efficiency. We first point out, with coarse quantization, the effective channel is imbalanced and thus no longer able to circularly shift the transmitted symbols along the delay-Doppler domain. Meanwhile, the effective channel is non-isotropic, which imposes a significant loss to symbol detection algorithms like the original approximate message passing (AMP). Although the algorithm of generalized expectation consistent for signal recovery (GEC-SR) can mitigate this loss, the complexity in computation is prohibitively high, mainly due to an dramatic increase in the matrix size of OTFS. In this context, we propose a low-complexity algorithm that incorporates into the GEC-SR a quick inversion of quasi-banded matrices, reducing the complexity from a cubic order to a linear order while keeping the performance at the same level.

The rational design of novel molecules with desired bioactivity is a critical but challenging task in drug discovery, especially when treating a novel target family or understudied targets. Here, we propose PGMG, a pharmacophore-guided deep learning approach for bioactivate molecule generation. Through the guidance of pharmacophore, PGMG provides a flexible strategy to generate bioactive molecules with structural diversity in various scenarios using a trained variational autoencoder. We show that PGMG can generate molecules matching given pharmacophore models while maintaining a high level of validity, uniqueness, and novelty. In the case studies, we demonstrate the application of PGMG to generate bioactive molecules in ligand-based and structure-based drug de novo design, as well as in lead optimization scenarios. Overall, the flexibility and effectiveness of PGMG make it a useful tool for accelerating the drug discovery process.