Search

The operating system (OS) is the backbone of modern computing, providing essential services and managing resources for computer hardware and software. This review paper offers an in-depth analysis of operating systems' evolution, current state, and prospects. We begin with an overview of the concept and significance of operating systems in the digital era. In the second section, we delve into the existing released operating systems, examining their architectures, functionalities, and the ecosystems they support. We then explore recent advances in OS evolution, highlighting innovations in real-time processing, distributed computing, and security. The third section focuses on the new era of operating systems, discussing emerging trends like the Internet of Things (IoT), cloud computing, and artificial intelligence (AI) integration. We also consider the challenges and opportunities presented by these developments. This review concludes with a synthesis of the current landscape and a forward-looking discussion on the future trajectories of operating systems, including open issues and areas ripe for further research and innovation. Finally, we put forward a new OS architecture.

Higher-Order Influence Functions (HOIF), developed in a series of papers over the past twenty years, is a fundamental theoretical device for constructing rate-optimal causal-effect estimators from observational studies. However, the value of HOIF for analyzing well-conducted randomized controlled trials (RCTs) has not been explicitly explored. In the recent U.S. Food and Drug Administration (FDA) and European Medicines Agency (EMA) guidelines on the practice of covariate adjustment in analyzing RCTs, in addition to the simple, unadjusted difference-in-mean estimator, it was also recommended to report the estimator adjusting for baseline covariates via a simple parametric working model, such as a linear model. In this paper, we show that a HOIF-motivated estimator for the treatment-specific mean has significantly improved statistical properties compared to popular adjusted estimators in practice when the number of baseline covariates $p$ is relatively large compared to the sample size $n$. We also characterize the conditions under which the HOIF-motivated estimator improves upon the unadjusted one. Furthermore, we demonstrate that a novel debiased adjusted estimator proposed recently by Lu et al. is, in fact, another HOIF-motivated estimator in disguise. Numerical and empirical studies are conducted to corroborate our theoretical findings., Comment: 73 pages, 13 figures, 1 table

Density wave (DW) order is believed to be correlated with superconductivity in the recently discovered high-temperature superconductor La$_3$Ni$_2$O$_7$. However, experimental investigations of its evolution under high pressure are still lacking. Here, we explore the quasiparticle dynamics in bilayer nickelate La$_3$Ni$_2$O$_7$ single crystals using ultrafast optical pump-probe spectroscopy under high pressures up to 34.2 GPa. At ambient pressure, the temperature-dependent relaxation dynamics demonstrate a phonon bottleneck effect due to the opening of an energy gap around 151 K. The energy scale of the DW-like gap is determined to be 66 meV by the Rothwarf-Taylor model. Combined with recent experiential results, we propose that this DW-like transition at ambient pressure and low temperature is spin density wave (SDW). With increasing pressure, this SDW order is significantly suppressed up to 13.3 GPa before it completely disappears around 26 GPa. Remarkably, at pressures above 29.4 GPa, we observe the emergence of another DW-like order with a transition temperature of approximately 135 K, which is probably related to the predicted charge density wave (CDW) order. Our study provides the experimental evidence of the evolution of the DW-like gap under high pressure, offering critical insights into the correlation between DW order and superconductivity in La$_3$Ni$_2$O$_7$., Comment: 8 pages, 4 figures

Utilizing ultrafast time-resolved pump-probe spectroscopy measurements, we investigate the photoinduced quasiparticle dynamics of the topological materials LaSbTe and CeSbTe. In LaSbTe, the relaxation of quasiparticles is dominated by two different mechanisms: electron-phonon coupling, and phonon-assisted electron-hole recombination. Significantly, the amplitude of photoinduced reflectivity related to the former one shows two pronounced peaks at 156 K and 263 K, indicating the occurrence of two charge density wave (CDW) phase transitions. The ultrafast responses of CeSbTe share a lot of similarities with LaSbTe, and an additional CDW phase transition at 154 K is revealed in CeSbTe. However, the slower relaxation of CeSbTe exhibits an exotic behavior that deviates from the typical phonon-assisted electron-hole recombination process, probably due to the imbalance between the electron- and hole-type carriers. Unlike LaSbTe, the relaxation times of CeSbTe vary slightly with the pump power, inferring the possible participation of 4$f$ electron in the decay process. In addition, two oscillation modes around 1 THz and 3 THz are identified in both LaSbTe and CeSbTe, which are mostly likely to be coherent phonon modes. These findings unravel the existence of multiple CDW orders in LaSbTe and CeSbTe, offering insights into the underlying physics of these systems.

Treatment effect estimation under unconfoundedness is a fundamental task in causal inference. In response to the challenge of analyzing high-dimensional datasets collected in substantive fields such as epidemiology, genetics, economics, and social sciences, various methods for treatment effect estimation with high-dimensional nuisance parameters (the outcome regression and the propensity score) have been developed in recent years. However, it is still unclear what is the necessary and sufficient sparsity condition on the nuisance parameters such that we can estimate the treatment effect at $1 / \sqrt{n}$-rate. In this paper, we propose a new Double-Calibration strategy that corrects the estimation bias of the nuisance parameter estimates computed by regularized high-dimensional techniques and demonstrate that the corresponding Doubly-Calibrated estimator achieves $1 / \sqrt{n}$-rate as long as one of the nuisance parameters is sparse with sparsity below $\sqrt{n} / \log p$, where $p$ denotes the ambient dimension of the covariates, whereas the other nuisance parameter can be arbitrarily complex and completely misspecified. The Double-Calibration strategy can also be applied to settings other than treatment effect estimation, e.g. regression coefficient estimation in the presence of a diverging number of controls in a semiparametric partially linear model, and local average treatment effect estimation with instrumental variables.

Reproducing the electronic phase diagram of strongly correlated high-transition-temperature (high-Tc) superconductors in materials other than Cu-, Fe-, and Ni-based compounds has been a challenging task. Only very recently, a few material systems have partially achieved this goal by band engineering. In this work, we combine film growth, charge transport, magnetometry, Terahertz Spectroscopy, Raman scattering, and Scanning Transmission Electron Microscopy to investigate superconductivity and the normal state of ZrNx, which reveals a phase diagram that bears extraordinary similarities to those of high-Tc superconductors. Remarkably, even though superconductivity of ZrNx can be characterized within the Bardeen-Cooper-Schrieffer paradigm and its normal state can be understood within the Fermi liquid framework, by tunning the N chemical concentration, we observe the evolution of a superconducting dome in the close vicinity of a strongly insulating state and a normal state resistivity mimics its counterpart of the high-Tc superconductors., Comment: 11 pages, 4 figures; high resolution figures at the end