Learning Predictive Safety Filter via Decomposition of Robust Invariant Set

Ensuring safety of nonlinear systems under model uncertainty and external disturbances is crucial, especially for real-world control tasks. Predictive methods such as robust model predictive control (RMPC) require solving nonconvex optimization problems online, which leads to high computational burden and poor scalability. Reinforcement learning (RL) works well with complex systems, but pays the price of losing rigorous safety guarantee. This paper presents a theoretical framework that bridges the advantages of both RMPC and RL to synthesize safety filters for nonlinear systems with state- and action-dependent uncertainty. We decompose the robust invariant set (RIS) into two parts: a target set that aligns with terminal region design of RMPC, and a reach-avoid set that accounts for the rest of RIS. We propose a policy iteration approach for robust reach-avoid problems and establish its monotone convergence. This method sets the stage for an adversarial actor-critic deep RL algorithm, which simultaneously synthesizes a reach-avoid policy network, a disturbance policy network, and a reach-avoid value network. The learned reach-avoid policy network is utilized to generate nominal trajectories for online verification, which filters potentially unsafe actions that may drive the system into unsafe regions when worst-case disturbances are applied. We formulate a second-order cone programming (SOCP) approach for online verification using system level synthesis, which optimizes for the worst-case reach-avoid value of any possible trajectories. The proposed safety filter requires much lower computational complexity than RMPC and still enjoys persistent robust safety guarantee. The effectiveness of our method is illustrated through a numerical example

PlanE: Representation Learning over Planar Graphs

Graph neural networks are prominent models for representation learning over graphs, where the idea is to iteratively compute representations of nodes of an input graph through a series of transformations in such a way that the learned graph function is isomorphism invariant on graphs, which makes the learned representations graph invariants. On the other hand, it is well-known that graph invariants learned by these class of models are incomplete: there are pairs of non-isomorphic graphs which cannot be distinguished by standard graph neural networks. This is unsurprising given the computational difficulty of graph isomorphism testing on general graphs, but the situation begs to differ for special graph classes, for which efficient graph isomorphism testing algorithms are known, such as planar graphs. The goal of this work is to design architectures for efficiently learning complete invariants of planar graphs. Inspired by the classical planar graph isomorphism algorithm of Hopcroft and Tarjan, we propose PlanE as a framework for planar representation learning. PlanE includes architectures which can learn complete invariants over planar graphs while remaining practically scalable. We empirically validate the strong performance of the resulting model architectures on well-known planar graph benchmarks, achieving multiple state-of-the-art results.Comment: Proceedings of the Thirty-Seventh Annual Conference on Advances in Neural Information Processing Systems (NeurIPS 2023). Code and data available at: https://github.com/ZZYSonny/Plan

Quantum Image Processing and Its Application to Edge Detection: Theory and Experiment

Processing of digital images is continuously gaining in volume and relevance, with concomitant demands on data storage, transmission and processing power. Encoding the image information in quantum-mechanical systems instead of classical ones and replacing classical with quantum information processing may alleviate some of these challenges. By encoding and processing the image information in quantum-mechanical systems, we here demonstrate the framework of quantum image processing, where a pure quantum state encodes the image information: we encode the pixel values in the probability amplitudes and the pixel positions in the computational basis states. Our quantum image representation reduces the required number of qubits compared to existing implementations, and we present image processing algorithms that provide exponential speed-up over their classical counterparts. For the commonly used task of detecting the edge of an image, we propose and implement a quantum algorithm that completes the task with only one single-qubit operation, independent of the size of the image. This demonstrates the potential of quantum image processing for highly efficient image and video processing in the big data era.Comment: 13 pages, including 9 figures and 5 appendixe

CSPRD: A Financial Policy Retrieval Dataset for Chinese Stock Market

In recent years, great advances in pre-trained language models (PLMs) have sparked considerable research focus and achieved promising performance on the approach of dense passage retrieval, which aims at retrieving relative passages from massive corpus with given questions. However, most of existing datasets mainly benchmark the models with factoid queries of general commonsense, while specialised fields such as finance and economics remain unexplored due to the deficiency of large-scale and high-quality datasets with expert annotations. In this work, we propose a new task, policy retrieval, by introducing the Chinese Stock Policy Retrieval Dataset (CSPRD), which provides 700+ prospectus passages labeled by experienced experts with relevant articles from 10k+ entries in our collected Chinese policy corpus. Experiments on lexical, embedding and fine-tuned bi-encoder models show the effectiveness of our proposed CSPRD yet also suggests ample potential for improvement. Our best performing baseline achieves 56.1% MRR@10, 28.5% NDCG@10, 37.5% Recall@10 and 80.6% Precision@10 on dev set

Distinct Roles of Perilipins in the Intramuscular Deposition of Lipids in Glutamine-Supplemented, Low-, and Normal-Birth-Weight Piglets

Piglets with low birth weight (LBW) usually have reduced muscle mass and increased lipid deposition compared with their normal-birth-weight (NBW) littermates. Supplementation of piglets with amino acids during the first days of life may improve muscle growth and simultaneously alter the intramuscular lipid deposition. The aim of the current study was to investigate the influence of glutamine (Gln) supplementation during the early suckling period on lipid deposition in the longissimus muscle (MLD) and the role of different perilipin (PLIN) family members in this process. Four groups were generated consisting of 72 male LBW piglets and 72 NBW littermates. Piglets were supplemented with either 1 g Gln/kg body weight or an isonitrogenous amount of alanine (Ala) between days post natum (dpn) 1 and 12. Twelve piglets per group were slaughtered at 5, 12, and 26 dpn, and muscle tissue was collected. Perilipins were localized by immunohistochemistry in muscle sections. The mRNA and protein abundances of PLIN family members and related lipases were quantified by quantitative RT-PCR (qPCR) and western blots, respectively. While PLIN1 was localized around lipid droplets in mature and developing adipocytes, PLIN2 was localized at intramyocellular lipid droplets, PLIN3 and 4 at cell membranes of muscle fibers and adipocytes, and PLIN5 in the cytoplasm of undefined cells. The western blot results indicated higher protein abundances of PLIN2, 3, 4, and 5 in LBW piglets (p < 0.05) at 5 dpn compared with their NBW littermates independent of supplementation, while not directly reflecting the mRNA expression levels. The mRNA abundance of PLIN2 was lower while PLIN4 was higher in piglets at 26 dpn in comparison with piglets at 5 dpn (p < 0.01). Relative mRNA expression of LPL and CGI-58 was lowest in piglets at 5 dpn (p < 0.001). However, ATGL mRNA was not influenced by birth weight or supplementation, but the Spearman correlation coefficient analysis revealed close correlations with PLIN2, 4, and 5 mRNA at 5 and 26 dpn (r > 0.5, p < 0.001). The results indicated the importance of birth weight and age for intramuscular lipid deposition and different roles of PLIN family members in this process, but no clear modulating effect of Gln supplementation

Non-Standard Errors

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: Non-standard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for better reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants