An optical clock based on a topological attractor in the polariton superfluid dynamics

We propose an optical polariton clock based on the topologically protected persistent oscillatory dynamics of a polariton superfluid, which is excited non-resonantly by a super-Gaussian laser beam in a semiconductor microcavity containing an external C-shape potential. The persistent oscillations, characterised by a topological attractor, are based on the dynamical behavior of small Josephson vortices rotating around the edge of the core of the central vortex. The clock demonstrates a remarkable stability towards perturbations and may be tuned by the pump laser intensity to two different frequency ranges: 20.16{\pm}0.14 GHz and 48.4{\pm}1.2 GHz. This clock generator is bistable due to the chirality of the vortex

SDSS-IV MaNGA: The Roles of AGNs and Dynamical Processes in Star Formation Quenching in Nearby Disk Galaxies

We study how star formation (SF) is quenched in low-redshift disk galaxies with integral-field spectroscopy. We select 131 face-on spiral galaxies with stellar mass greater than $\rm 3\times10^{10}M_\odot$, and with spatially resolved spectrum from MaNGA DR13. We subdivide the sample into four groups based on the offset of their global specific star formation rate (SFR) from the star-forming main sequence and stack the radial profiles of stellar mass and SFR. By comparing the stacked profiles of quiescent and star-forming disk galaxies, we find that the decrease of the global SFR is caused by the suppression of SF at all radii, but with a more significant drop from the center to the outer regions following an inside-out pattern. As the global specific SFR decreases, the central stellar mass, the fraction of disk galaxies hosting stellar bars, and active galactic nuclei (AGNs; including both LINERs and Seyferts) all increase, indicating dynamical processes and AGN feedback are possible contributors to the inside-out quenching of SF in the local universe. However, if we include only Seyferts, or AGNs with ${\rm EW(H\alpha)>3\AA}$, the increasing trend of AGN fraction with decreasing global sSFR disappears. Therefore, if AGN feedback is contributing to quenching, we suspect that it operates in the low-luminosity AGN mode, as indicated by the increasing large bulge mass of the more passive disk galaxies.Comment: 12 pages, 7 figures, published in ApJ, typos corrected, references update

Exploring the interfacial coupling between graphene and the antiferromagnetic insulator MnPSe3_3

Interfacial coupling between graphene and other 2D materials can give rise to intriguing physical phenomena. In particular, several theoretical studies predict that the interplay between graphene and an antiferromagnetic insulator could lead to the emergence of quantum anomalous Hall phases. However, such phases have not been observed experimentally yet, and further experimental studies are needed to reveal the interaction between graphene and antiferromagnetic insulators. Here, we report the study in heterostructures composed of graphene and the antiferromagnetic insulator MnPSe$_3$. It is found that the MnPSe$_3$ has little impact on the quantum Hall phases apart from doping graphene via interfacial charge transfer. However, the magnetic order can contribute indirectly via process like Kondo effect, as evidenced by the observed minimum in the temperature-resistance curve between 20-40 K, far below the N\'eel temperature (70 K)

Gut microbiota in perioperative neurocognitive disorders: current evidence and future directions

Perioperative neurocognitive disorders (PND) is a common surgical anesthesia complication characterized by impairment of memory, attention, language understanding and social ability, which can lead to a decline in the quality of life of patients, prolong the hospitalization period and increase the mortality rate. PND has a high incidence rate, which has a great impact on postoperative recovery and quality of life of patients, and has caused a heavy economic burden to society and families. In recent years, PND has become an important public health problem. The high risk population of PND is more prone to gut microbiota imbalance, and gut microbiota may also affect the inflammatory response of the central nervous system through the microbiota-gut-brain axis. Meanwhile, Neuroinflammation and immune activation are important mechanisms of PND. Regulating gut microbiota through probiotics or fecal bacteria transplantation can significantly reduce neuroinflammation, reduce the abnormal activation of immune system and prevent the occurrence of PND. This review summarizes the research progress of gut microbiota and PND, providing basis for the prevention and treatment of PND

Pursuing Equilibrium of Medical Resources via Data Empowerment in Parallel Healthcare System

The imbalance between the supply and demand of healthcare resources is a global challenge, which is particularly severe in developing countries. Governments and academic communities have made various efforts to increase healthcare supply and improve resource allocation. However, these efforts often remain passive and inflexible. Alongside these issues, the emergence of the parallel healthcare system has the potential to solve these problems by unlocking the data value. The parallel healthcare system comprises Medicine-Oriented Operating Systems (MOOS), Medicine-Oriented Scenario Engineering (MOSE), and Medicine-Oriented Large Models (MOLMs), which could collect, circulate, and empower data. In this paper, we propose that achieving equilibrium in medical resource allocation is possible through parallel healthcare systems via data empowerment. The supply-demand relationship can be balanced in parallel healthcare systems by (1) increasing the supply provided by digital and robotic doctors in MOOS, (2) identifying individual and potential demands by proactive diagnosis and treatment in MOSE, and (3) improving supply-demand matching using large models in MOLMs. To illustrate the effectiveness of this approach, we present a case study optimizing resource allocation from the perspective of facility accessibility. Results demonstrate that the parallel healthcare system could result in up to 300% improvement in accessibility

Communication-Efficient Topologies for Decentralized Learning with O(1)O(1) Consensus Rate

Decentralized optimization is an emerging paradigm in distributed learning in which agents achieve network-wide solutions by peer-to-peer communication without the central server. Since communication tends to be slower than computation, when each agent communicates with only a few neighboring agents per iteration, they can complete iterations faster than with more agents or a central server. However, the total number of iterations to reach a network-wide solution is affected by the speed at which the agents' information is ``mixed'' by communication. We found that popular communication topologies either have large maximum degrees (such as stars and complete graphs) or are ineffective at mixing information (such as rings and grids). To address this problem, we propose a new family of topologies, EquiTopo, which has an (almost) constant degree and a network-size-independent consensus rate that is used to measure the mixing efficiency. In the proposed family, EquiStatic has a degree of $\Theta(\ln(n))$, where $n$ is the network size, and a series of time-dependent one-peer topologies, EquiDyn, has a constant degree of 1. We generate EquiDyn through a certain random sampling procedure. Both of them achieve an $n$-independent consensus rate. We apply them to decentralized SGD and decentralized gradient tracking and obtain faster communication and better convergence, theoretically and empirically. Our code is implemented through BlueFog and available at \url{https://github.com/kexinjinnn/EquiTopo}Comment: NeurIPS 202

Dynamic estimation of epidemiological parameters of COVID-19 outbreak and effects of interventions on its spread

Background: A key challenge in estimating epidemiological parameters for a pandemic such as the initial COVID-19 outbreak in Wuhan is the discrepancy between the officially reported number of infections and the true number of infections. A common approach to tackling the challenge is to use the number of infections exported from the originating city to infer the true number. This approach can only provide a static estimate of the epidemiological parameters before city lockdown because there are almost no exported cases thereafter.Methods: We propose a Bayesian estimation method that dynamically estimates the epidemiological parameters by recovering true numbers of infections from day-to-day official numbers. To illustrate the use of this method, we provide a comprehensive retrospection on how the COVID-19 had progressed in Wuhan from January 19 to March 5, 2020. Particularly, we estimate that the outbreak sizes by January 23 and March 5 were 11,239 [95% CI 4,794–22,372] and 124,506 [95% CI 69,526–265,113], respectively.Results: The effective reproduction number attained its maximum on January 24 (3.42 [95% CI 3.34–3.50]) and became less than 1 from February 7 (0.76 [95% CI 0.65–0.92]). We also estimate the effects of two major government interventions on the spread of COVID-19 in Wuhan.Conclusions: This case study by our proposed method affirms the believed importance and effectiveness of imposing tight non-essential travel restrictions and affirm the importance and effectiveness of government interventions (e.g., transportation suspension and large scale hospitalization) for effective mitigation of COVID-19 community spread

In silico analyses for potential key genes associated with gastric cancer

Background Understanding hub genes involved in gastric cancer (GC) metastasis could lead to effective approaches to diagnose and treat cancer. In this study, we aim to identify the hub genes and investigate the underlying molecular mechanisms of GC. Methods To explore potential therapeutic targets for GC,three expression profiles (GSE54129, GSE33651, GSE81948) of the genes were extracted from the Gene Expression Omnibus (GEO) database. The GEO2R online tool was applied to screen out differentially expressed genes (DEGs) between GC and normal gastric samples. Database for Annotation, Visualization and Integrated Discovery was applied to perform Gene Ontology (GO) and KEGG pathway enrichment analysis. The protein-protein interaction (PPI) network of these DEGs was constructed using a STRING online software. The hub genes were identified by the CytoHubba plugin of Cytoscape software. Then, the prognostic value of these identified genes was verified by gastric cancer database derived from Kaplan-Meier plotter platform. Results A total of 85 overlapped upregulated genes and 44 downregulated genes were identified. The majority of the DEGs were enriched in extracellular matrix organization, endodermal cell differentiation, and endoderm formation. Moreover, five KEGG pathways were significantly enriched, including ECM-receptor interaction, amoebiasis, AGE-RAGE signaling pathway in diabetic complications, focal adhesion, protein digestion and absorption. By combining the results of PPI network and CytoHubba, a total of nine hub genes including COL1A1, THBS1, MMP2, CXCL8, FN1, TIMP1, SPARC, COL4A1, and ITGA5 were selected. The Kaplan-Meier plotter database confirmed that overexpression levels of these genes were associated with reduced overall survival, except for THBS1 and CXCL8. Conclusions Our study suggests that COL1A1, MMP2, FN1, TIMP1, SPARC, COL4A1, and ITGA5 may be potential biomarkers and therapeutic targets for GC. Further study is needed to assess the effect of THBS1 and CXCL8 on GC

Fake Alignment: Are LLMs Really Aligned Well?

The growing awareness of safety concerns in large language models (LLMs) has sparked considerable interest in the evaluation of safety within current research endeavors. This study investigates an interesting issue pertaining to the evaluation of LLMs, namely the substantial discrepancy in performance between multiple-choice questions and open-ended questions. Inspired by research on jailbreak attack patterns, we argue this is caused by mismatched generalization. That is, the LLM does not have a comprehensive understanding of the complex concept of safety. Instead, it only remembers what to answer for open-ended safety questions, which makes it unable to solve other forms of safety tests. We refer to this phenomenon as fake alignment and construct a comparative benchmark to empirically verify its existence in LLMs. Such fake alignment renders previous evaluation protocols unreliable. To address this, we introduce the Fake alIgNment Evaluation (FINE) framework and two novel metrics--Consistency Score (CS) and Consistent Safety Score (CSS), which jointly assess two complementary forms of evaluation to quantify fake alignment and obtain corrected performance estimates. Applying FINE to 14 widely-used LLMs reveals several models with purported safety are poorly aligned in practice. Our work highlights potential limitations in prevailing alignment methodologies