M\"{o}bius disjointness for a class of exponential functions

A vast class of exponential functions is showed to be deterministic. This class includes functions whose exponents are polynomial-like or "piece-wise" close to polynomials after differentiation. Many of these functions are indeed disjoint from the M\"obius function. As a consequence, we show that Sarnak's Disjointness Conjecture for the M\"obius function (from deterministic sequences) is equivalent to the disjointness in average over short intervalsComment: 37 pages. To better understand the main results of this paper, we split it into two independent papers. The second paper is arXiv:2101.1013

Sharp high-probability sample complexities for policy evaluation with linear function approximation

This paper is concerned with the problem of policy evaluation with linear function approximation in discounted infinite horizon Markov decision processes. We investigate the sample complexities required to guarantee a predefined estimation error of the best linear coefficients for two widely-used policy evaluation algorithms: the temporal difference (TD) learning algorithm and the two-timescale linear TD with gradient correction (TDC) algorithm. In both the on-policy setting, where observations are generated from the target policy, and the off-policy setting, where samples are drawn from a behavior policy potentially different from the target policy, we establish the first sample complexity bound with high-probability convergence guarantee that attains the optimal dependence on the tolerance level. We also exhihit an explicit dependence on problem-related quantities, and show in the on-policy setting that our upper bound matches the minimax lower bound on crucial problem parameters, including the choice of the feature maps and the problem dimension.Comment: The first two authors contributed equall

AIO-P: Expanding Neural Performance Predictors Beyond Image Classification

Evaluating neural network performance is critical to deep neural network design but a costly procedure. Neural predictors provide an efficient solution by treating architectures as samples and learning to estimate their performance on a given task. However, existing predictors are task-dependent, predominantly estimating neural network performance on image classification benchmarks. They are also search-space dependent; each predictor is designed to make predictions for a specific architecture search space with predefined topologies and set of operations. In this paper, we propose a novel All-in-One Predictor (AIO-P), which aims to pretrain neural predictors on architecture examples from multiple, separate computer vision (CV) task domains and multiple architecture spaces, and then transfer to unseen downstream CV tasks or neural architectures. We describe our proposed techniques for general graph representation, efficient predictor pretraining and knowledge infusion techniques, as well as methods to transfer to downstream tasks/spaces. Extensive experimental results show that AIO-P can achieve Mean Absolute Error (MAE) and Spearman's Rank Correlation (SRCC) below 1% and above 0.5, respectively, on a breadth of target downstream CV tasks with or without fine-tuning, outperforming a number of baselines. Moreover, AIO-P can directly transfer to new architectures not seen during training, accurately rank them and serve as an effective performance estimator when paired with an algorithm designed to preserve performance while reducing FLOPs.Comment: AAAI 2023 Oral Presentation; version includes supplementary material; 16 Pages, 4 Figures, 22 Table

6

6′-O-galloylpaeoniflorin (GPF), a galloylated derivative of paeoniflorin isolated from peony root, has been proven to possess antioxidant potential. In this present study, we revealed that GPF treatment exerted significant neuroprotection of PC12 cells following OGD, as evidenced by a reduction of oxidative stress, inflammatory response, cellular injury, and apoptosis in vitro. Furthermore, treatment with GPF increased the levels of phosphorylated Akt (p-Akt) and nuclear factor-erythroid 2-related factor 2 (Nrf2), as well as promoted Nrf2 translocation in PC12 cells, which could be inhibited by Ly294002, an inhibitor of phosphoinositide 3-kinase (PI3K). In addition, Nrf2 knockdown or Ly294002 treatment significantly attenuated the antioxidant, anti-inflammatory, and antiapoptotic activities of GPF in vitro. In vivo studies indicated that GPF treatment significantly reduced infarct volume and improved neurological deficits in rats subjected to CIRI, as well as decreased oxidative stress, inflammation, and apoptosis, which could be inhibited by administration of Ly294002. In conclusion, these results revealed that GPF possesses neuroprotective effects against oxidative stress, inflammation, and apoptosis after ischemia-reperfusion insult via activation of the PI3K/Akt/Nrf2 pathway

Schizophrenia-associated somatic copy-number variants from 12,834 cases reveal recurrent NRXN1 and ABCB11 disruptions

While germline copy-number variants (CNVs) contribute to schizophrenia (SCZ) risk, the contribution of somatic CNVs (sCNVs)—present in some but not all cells—remains unknown. We identified sCNVs using blood-derived genotype arrays from 12,834 SCZ cases and 11,648 controls, filtering sCNVs at loci recurrently mutated in clonal blood disorders. Likely early-developmental sCNVs were more common in cases (0.91%) than controls (0.51%, p = 2.68e−4), with recurrent somatic deletions of exons 1–5 of the NRXN1 gene in five SCZ cases. Hi-C maps revealed ectopic, allele-specific loops forming between a potential cryptic promoter and non-coding cis-regulatory elements upon 5′ deletions in NRXN1. We also observed recurrent intragenic deletions of ABCB11, encoding a transporter implicated in anti-psychotic response, in five treatment-resistant SCZ cases and showed that ABCB11 is specifically enriched in neurons forming mesocortical and mesolimbic dopaminergic projections. Our results indicate potential roles of sCNVs in SCZ risk

Hofmeister Effects Shine in Nanoscience

Abstract Hofmeister effects play a crucial role in nanoscience by affecting the physicochemical and biochemical processes. Thus far, numerous wonderful applications from various aspects of nanoscience have been developed based on the mechanism of Hofmeister effects, such as hydrogel/aerogel engineering, battery design, nanosynthesis, nanomotors, ion sensors, supramolecular chemistry, colloid and interface science, nanomedicine, and transport behaviors, etc. In this review, for the first time, the progress of applying Hofmeister effects is systematically introduced and summarized in nanoscience. It is aimed to provide a comprehensive guideline for future researchers to design more useful Hofmeister effects‐based nanosystems

Light-driven Nanomotors/robots for Environmental and Biomedical Applications

In recent years, micro/nanomotors have become an important research field in nanotechnology. Light-driven micro/nanomotors have received extensive attention due to the advantages of high controllability, good programmability, and easy operation, as well as their potential in environmental remediation and biomedical applications. Herein, Chapter 1 introduces the background and motion mechanism of the field of light-driven micro/nanomotors, the design principles of light-driven micro/nanomotor materials, and the motion modes of individual motors and group motors. In addition, the current development and applications in this field are also discussed. The second chapter of the dissertation will focus on the work of applying single-atom catalysis to enhance the performance of nanomotors and the related application. The third chapter of the dissertation will discuss the work of developing light-driven SrTiO3:Al-based nanomotors for antimicrobial applications. The last chapter of the dissertation will summarize the conclusion of this dissertation and serve as a paradigm to rationally design novel light-driven nano/micromotors based on cutting-edge photocatalysts with outstanding catalytic performance for potential environmental and biomedical applications

Light-driven Nanomotors/robots for Environmental and Biomedical Applications

In recent years, micro/nanomotors have become an important research field in nanotechnology. Light-driven micro/nanomotors have received extensive attention due to the advantages of high controllability, good programmability, and easy operation, as well as their potential in environmental remediation and biomedical applications. Herein, Chapter 1 introduces the background and motion mechanism of the field of light-driven micro/nanomotors, the design principles of light-driven micro/nanomotor materials, and the motion modes of individual motors and group motors. In addition, the current development and applications in this field are also discussed. The second chapter of the dissertation will focus on the work of applying single-atom catalysis to enhance the performance of nanomotors and the related application. The third chapter of the dissertation will discuss the work of developing light-driven SrTiO3:Al-based nanomotors for antimicrobial applications. The last chapter of the dissertation will summarize the conclusion of this dissertation and serve as a paradigm to rationally design novel light-driven nano/micromotors based on cutting-edge photocatalysts with outstanding catalytic performance for potential environmental and biomedical applications