Bis(3-hy­droxy­propanaminium) naphthalene-1,5-disulfonate

In the title molecular salt, 2C3H10NO+·C10H6O6S2 2−, the cations and anions are associated via N—H⋯O and O—H⋯O hydrogen-bonding inter­actions, giving rise to a three-dimensional structure with zigzag rows of cations lying between rows of anions. The asymmetric unit contains one cation and one half-anion, which is related to the remainder of the mol­ecule by an inversion center

Deep Learning Based on Orthogonal Approximate Message Passing for CP-Free OFDM

Channel estimation and signal detection are very challenging for an orthogonal frequency division multiplexing (OFDM) system without cyclic prefix (CP). In this article, deep learning based on orthogonal approximate message passing (DL-OAMP) is used to address these problems. The DL-OAMP receiver includes a channel estimation neural network (CE-Net) and a signal detection neural network based on OAMP, called OAMP-Net. The CE-Net is initialized by the least square channel estimation algorithm and refined by minimum mean-squared error (MMSE) neural network. The OAMP-Net is established by unfolding the iterative OAMP algorithm and adding some trainable parameters to improve the detection performance. The DL-OAMP receiver is with low complexity and can estimate time-varying channels with only a single training. Simulation results demonstrate that the bit-error rate (BER) of the proposed scheme is lower than those of competitive algorithms for high-order modulation.Comment: 5 pages, 4 figures, updated manuscript, International Conference on Acoustics, Speech and Signal Processing (ICASSP 2019). arXiv admin note: substantial text overlap with arXiv:1903.0476

Bis(diisopropyl­ammonium) naphthalene-1,5-disulfonate

In the title compound, 2C6H16N+·C10H6O6S2 2−, the cations and anions are associated via N—H⋯O and C—H⋯O hydrogen-bonding inter­actions

Causal Reinforcement Learning: An Instrumental Variable Approach

In the standard data analysis framework, data is first collected (once for all), and then data analysis is carried out. With the advancement of digital technology, decisionmakers constantly analyze past data and generate new data through the decisions they make. In this paper, we model this as a Markov decision process and show that the dynamic interaction between data generation and data analysis leads to a new type of bias -- reinforcement bias -- that exacerbates the endogeneity problem in standard data analysis. We propose a class of instrument variable (IV)-based reinforcement learning (RL) algorithms to correct for the bias and establish their asymptotic properties by incorporating them into a two-timescale stochastic approximation framework. A key contribution of the paper is the development of new techniques that allow for the analysis of the algorithms in general settings where noises feature time-dependency. We use the techniques to derive sharper results on finite-time trajectory stability bounds: with a polynomial rate, the entire future trajectory of the iterates from the algorithm fall within a ball that is centered at the true parameter and is shrinking at a (different) polynomial rate. We also use the technique to provide formulas for inferences that are rarely done for RL algorithms. These formulas highlight how the strength of the IV and the degree of the noise's time dependency affect the inference.Comment: main body: 38 pages; supplemental material: 58 page

Learning Excavation of Rigid Objects with Offline Reinforcement Learning

Autonomous excavation is a challenging task. The unknown contact dynamics between the excavator bucket and the terrain could easily result in large contact forces and jamming problems during excavation. Traditional model-based methods struggle to handle such problems due to complex dynamic modeling. In this paper, we formulate the excavation skills with three novel manipulation primitives. We propose to learn the manipulation primitives with offline reinforcement learning (RL) to avoid large amounts of online robot interactions. The proposed method can learn efficient penetration skills from sub-optimal demonstrations, which contain sub-trajectories that can be ``stitched" together to formulate an optimal trajectory without causing jamming. We evaluate the proposed method with extensive experiments on excavating a variety of rigid objects and demonstrate that the learned policy outperforms the demonstrations. We also show that the learned policy can quickly adapt to unseen and challenging fragmented rocks with online fine-tuning.Comment: Submitted to IROS 202

Tetra­aqua­bis­(2-methyl-1H-imidazole-κN 3)cobalt(II) naphthalene-1,5-disulfonate

In the title complex, [Co(C4H6N2)2(H2O)4](C10H6O6S2), the cation and anion both reside on crystallographic inversion centers, such that the asymmetric unit comprises one half cation and one half anion. The central CoII ion is coordinated by four water mol­ecules and two 2-methyl­imidazole ligands, resulting in a trans-octa­hedral coordination geometry. The existence of strong N—H⋯O and O—H⋯O hydrogen-bonding inter­actions gives rise to a three-dimensional structure