Symbol: Generating Flexible Black-Box Optimizers through Symbolic Equation Learning

Recent Meta-learning for Black-Box Optimization (MetaBBO) methods harness neural networks to meta-learn configurations of traditional black-box optimizers. Despite their success, they are inevitably restricted by the limitations of predefined hand-crafted optimizers. In this paper, we present \textsc{Symbol}, a novel framework that promotes the automated discovery of black-box optimizers through symbolic equation learning. Specifically, we propose a Symbolic Equation Generator (SEG) that allows closed-form optimization rules to be dynamically generated for specific tasks and optimization steps. Within \textsc{Symbol}, we then develop three distinct strategies based on reinforcement learning, so as to meta-learn the SEG efficiently. Extensive experiments reveal that the optimizers generated by \textsc{Symbol} not only surpass the state-of-the-art BBO and MetaBBO baselines, but also exhibit exceptional zero-shot generalization abilities across entirely unseen tasks with different problem dimensions, population sizes, and optimization horizons. Furthermore, we conduct in-depth analyses of our \textsc{Symbol} framework and the optimization rules that it generates, underscoring its desirable flexibility and interpretability.Comment: Published as a conference paper at ICLR 202

MetaBox: A Benchmark Platform for Meta-Black-Box Optimization with Reinforcement Learning

Recently, Meta-Black-Box Optimization with Reinforcement Learning (MetaBBO-RL) has showcased the power of leveraging RL at the meta-level to mitigate manual fine-tuning of low-level black-box optimizers. However, this field is hindered by the lack of a unified benchmark. To fill this gap, we introduce MetaBox, the first benchmark platform expressly tailored for developing and evaluating MetaBBO-RL methods. MetaBox offers a flexible algorithmic template that allows users to effortlessly implement their unique designs within the platform. Moreover, it provides a broad spectrum of over 300 problem instances, collected from synthetic to realistic scenarios, and an extensive library of 19 baseline methods, including both traditional black-box optimizers and recent MetaBBO-RL methods. Besides, MetaBox introduces three standardized performance metrics, enabling a more thorough assessment of the methods. In a bid to illustrate the utility of MetaBox for facilitating rigorous evaluation and in-depth analysis, we carry out a wide-ranging benchmarking study on existing MetaBBO-RL methods. Our MetaBox is open-source and accessible at: https://github.com/GMC-DRL/MetaBox.Comment: Accepted at NuerIPS 202

An Orthogonal Learning Differential Evolution Algorithm for Remote Sensing Image Registration

We introduce an area-based method for remote sensing image registration. We use orthogonal learning differential evolution algorithm to optimize the similarity metric between the reference image and the target image. Many local and global methods have been used to achieve the optimal similarity metric in the last few years. Because remote sensing images are usually influenced by large distortions and high noise, local methods will fail in some cases. For this reason, global methods are often required. The orthogonal learning (OL) strategy is efficient when searching in complex problem spaces. In addition, it can discover more useful information via orthogonal experimental design (OED). Differential evolution (DE) is a heuristic algorithm. It has shown to be efficient in solving the remote sensing image registration problem. So orthogonal learning differential evolution algorithm (OLDE) is efficient for many optimization problems. The OLDE method uses the OL strategy to guide the DE algorithm to discover more useful information. Experiments show that the OLDE method is more robust and efficient for registering remote sensing images

Heat Shock Protein 70 Protects the Heart from Ischemia/Reperfusion Injury through Inhibition of p38 MAPK Signaling.

BackgroundHeat shock protein 70 (Hsp70) has been shown to exert cardioprotection. Intracellular calcium ([Ca2+]i) overload induced by p38 mitogen-activated protein kinase (p38 MAPK) activation contributes to cardiac ischemia/reperfusion (I/R) injury. However, whether Hsp70 interacts with p38 MAPK signaling is unclear. Therefore, this study investigated the regulation of p38 MAPK by Hsp70 in I/R-induced cardiac injury.MethodsNeonatal rat cardiomyocytes were subjected to oxygen-glucose deprivation for 6 h followed by 2 h reoxygenation (OGD/R), and rats underwent left anterior artery ligation for 30 min followed by 30 min of reperfusion. The p38 MAPK inhibitor (SB203580), Hsp70 inhibitor (Quercetin), and Hsp70 short hairpin RNA (shRNA) were used prior to OGD/R or I/R. Cell viability, lactate dehydrogenase (LDH) release, serum cardiac troponin I (cTnI), [Ca2+]i levels, cell apoptosis, myocardial infarct size, mRNA level of IL-1β and IL-6, and protein expression of Hsp70, phosphorylated p38 MAPK (p-p38 MAPK), sarcoplasmic/endoplasmic reticulum Ca2+-ATPase2 (SERCA2), phosphorylated signal transducer and activator of transcription3 (p-STAT3), and cleaved caspase3 were assessed.ResultsPretreatment with a p38 MAPK inhibitor, SB203580, significantly attenuated OGD/R-induced cell injury or I/R-induced myocardial injury, as evidenced by improved cell viability and lower LDH release, resulted in lower serum cTnI and myocardial infarct size, alleviation of [Ca2+]i overload and cell apoptosis, inhibition of IL-1β and IL-6, and modulation of protein expressions of p-p38 MAPK, SERCA2, p-STAT3, and cleaved-caspase3. Knockdown of Hsp70 by shRNA exacerbated OGD/R-induced cell injury, which was effectively abolished by SB203580. Moreover, inhibition of Hsp70 by quercetin enhanced I/R-induced myocardial injury, while SB203580 pretreatment reversed the harmful effects caused by quercetin.ConclusionsInhibition of Hsp70 aggravates [Ca2+]i overload, inflammation, and apoptosis through regulating p38 MAPK signaling during cardiac I/R injury, which may help provide novel insight into cardioprotective strategies

Multi-scale analysis of schizophrenia risk genes, brain structure, and clinical symptoms reveals integrative clues for subtyping schizophrenia patients

Analysis linking directly genomics, neuroimaging phenotypes and clinical measurements is crucial for understanding psychiatric disorders, but remains rare. Here, we describe a multi-scale analysis using genome-wide SNPs, gene-expression, grey matter volume (GMV) and the Positive and Negative Syndrome Scale scores (PANSS) to explore the etiology of schizophrenia. With 72 drug-naive schizophrenic first episode patients (FEPs) and 73 matched heathy controls, we identified 108 genes, from schizophrenia risk genes, that correlated significantly with GMV, which are highly co-expressed in the brain during development. Among these 108 candidates, 19 distinct genes were found associated with 16 brain regions referred to as hot clusters (HCs), primarily in the frontal cortex, sensory-motor regions and temporal and parietal regions. The patients were subtyped into three groups with distinguishable PANSS scores by the GMV of the identified HCs. Furthermore, we found that HCs with common GMV among patient groups are related to genes that mostly mapped to pathways relevant to neural signaling, which are associated with the risk for schizophrenia. Our results provide an integrated view of how genetic variants may affect brain structures that lead to distinct disease phenotypes. The method of multi-scale analysis that was described in this research, may help to advance the understanding of the etiology of schizophrenia

Relativistic Artificial Molecules Realized by Two Coupled Graphene Quantum Dots

Coupled quantum dots (QDs), usually referred to as artificial molecules, are important not only in exploring fundamental physics of coupled quantum objects, but also in realizing advanced QD devices. However, previous studies have been limited to artificial molecules with nonrelativistic fermions. Here, we show that relativistic artificial molecules can be realized when two circular graphene QDs are coupled to each other. Using scanning tunneling microscopy (STM) and spectroscopy (STS), we observe the formation of bonding and antibonding states of the relativistic artificial molecule and directly visualize these states of the two coupled graphene QDs. The formation of the relativistic molecular states strongly alters distributions of massless Dirac fermions confined in the graphene QDs. Because of the relativistic nature of the molecular states, our experiment demonstrates that the degeneracy of different angular-momentum states in the relativistic artificial molecule can be further lifted by external magnetic fields. Then, both the bonding and antibonding states are split into two peaks

Risk spillover between Bitcoin and conventional financial markets : an expectile-based approach

In order to challenge the existing literature that points to the detachment of Bitcoin from the global financial system, we use daily data from August 17, 2011–February 14, 2020 and apply a risk spillover approach based on expectiles. Results show reasonable evidence to imply the existence of downside risk spillover between Bitcoin and four assets (equities, bonds, currencies, and commodities), which seems to be time dependent. Our main findings have implications for participants in both the Bitcoin and traditional financial markets for the sake of asset allocation, and risk management. For policy makers, the findings suggest that Bitcoin should be monitored carefully for the sake of financial stability.National Natural Science Foundation of China, National Program for Support of Top-notch Young Professionals, Changjiang Scholars Program of the Ministry of Education of China, and Hunan Youth Talent Program.https://www.elsevier.com/locate/najefhj2022Economic

Local nearly non-strained perovskite lattice approaching a broad environmental stability window of efficient solar cells

Twist and fracture of surface lattice tend to occur under harsh condition due to the soft lattice natures of hybrid perovskite materials. Accordingly, surface defects and lattice distortion are produced, which allow the performance loss and notorious degradation in perovskite solar cells (PSCs). In our work, judiciously selected conjugated ligand was employed as the film intermediary, from which rigid and delocalization 4-phenylpyridine (4-pPy) exhibited the most significant improvement on both optoelectrical performance and stability of PSCs. By regulating the film crystallization kinetics, high-quality perovskite films can be obtained with preferable crystal orientation. Moreover, benefiting from the defects passivation and unidirectional bonding effect, coordinated 4-pPy “scaffold” on the lattice surface could mitigate vacancy formation and lattice twist/fracture under severe conditions. The resulted p-i-n planar device shows a considerable PCE of 21.12% (certified 20.2%) with negligible hysteresis, as well as an excellent storage (90% of original PCE after 1000 h at 60% RH), operating (90% of original PCE after 600 h at maximum power point) and thermal stress (89% of original PCE after 500 h at 85 °C) stability. It is hoped that our findings could open a new way to accelerate continued progress on PSCs regimes for efficieny maximization and stability prolongation

Molecular Basis of Efficient Replication and Pathogenicity of H9N2 Avian Influenza Viruses in Mice

H9N2 subtype avian influenza viruses (AIVs) have shown expanded host range and can infect mammals, such as humans and swine. To date the mechanisms of mammalian adaptation and interspecies transmission of H9N2 AIVs remain poorly understood. To explore the molecular basis determining mammalian adaptation of H9N2 AIVs, we compared two avian field H9N2 isolates in a mouse model: one (A/chicken/Guangdong/TS/2004, TS) is nonpathogenic, another one (A/chicken/Guangdong/V/2008, V) is lethal with efficient replication in mouse brains. In order to determine the basis of the differences in pathogenicity and brain tropism between these two viruses, recombinants with a single gene from the TS (or V) virus in the background of the V (or TS) virus were generated using reverse genetics and evaluated in a mouse model. The results showed that the PB2 gene is the major factor determining the virulence in the mouse model although other genes also have variable impacts on virus replication and pathogenicity. Further studies using PB2 chimeric viruses and mutated viruses with a single amino acid substitution at position 627 [glutamic acid (E) to lysine, (K)] in PB2 revealed that PB2 627K is critical for pathogenicity and viral replication of H9N2 viruses in mouse brains. All together, these results indicate that the PB2 gene and especially position 627 determine virus replication and pathogenicity in mice. This study provides insights into the molecular basis of mammalian adaptation and interspecies transmission of H9N2 AIVs