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The area under the curve (AUC) of the mean cumulative function (MCF) has recently been introduced as a novel estimand for evaluating treatment effects in recurrent event settings, capturing a totality of evidence in relation to disease progression. While the Lin-Wei-Yang-Ying (LWYY) model is commonly used for analyzing recurrent events, it relies on the proportional rate assumption between treatment arms, which is often violated in practice. In contrast, the AUC under MCFs does not depend on such proportionality assumptions and offers a clinically interpretable measure of treatment effect. To improve the precision of the AUC estimation while preserving its unconditional interpretability, we propose a nonparametric covariate adjustment approach. This approach guarantees efficiency gain compared to unadjusted analysis, as demonstrated by theoretical asymptotic distributions, and is universally applicable to various randomization schemes, including both simple and covariate-adaptive designs. Extensive simulations across different scenarios further support its advantage in increasing statistical power. Our findings highlight the importance of covariate adjustment for the analysis of AUC in recurrent event settings, offering practical guidance for its application in randomized clinical trials.

We examine the stability of a spherically symmetric regular black hole when subjected to perturbations from a charged scalar field. This particular black hole is constructed by deforming the Minkowski spacetime. It has been observed that the charged superradiant instability arises only within a specific range of the deformation parameter, potentially resulting in an instability growth rate with a maximum magnitude of approximately $\text{Im} (M \omega) \sim 10^{-3}$. This growth rate significantly exceeds the instability identified in ABG black holes discussed in prior research, suggesting a notable timescale for detecting this phenomenon in astrophysical scenarios. Additionally, we conduct a thorough investigation into how the three parameters of the model influence the onset and intensity of the instability. Our analysis offers further insights into the possible emergence of this instability in spherically regular black holes and its association with the nonlinear effects of the electromagnetic field., Comment: 19 pages,7 figures

Large language models (LLMs) like GPTs, trained on vast datasets, have demonstrated impressive capabilities in language understanding, reasoning, and planning, achieving human-level performance in various tasks. Most studies focus on enhancing these models by training on ever-larger datasets to build more powerful foundation models. While training stronger models is important, enabling models to evolve during inference is equally crucial, a process we refer to as AI self-evolution. Unlike large-scale training, self-evolution may rely on limited data or interactions. Inspired by the columnar organization of the human cerebral cortex, we hypothesize that AI models could develop cognitive abilities and build internal representations through iterative interactions with their environment. To achieve this, models need long-term memory (LTM) to store and manage processed interaction data. LTM supports self-evolution by representing diverse experiences across environments and agents. In this report, we explore AI self-evolution and its potential to enhance models during inference. We examine LTM's role in lifelong learning, allowing models to evolve based on accumulated interactions. We outline the structure of LTM and the systems needed for effective data retention and representation. We also classify approaches for building personalized models with LTM data and show how these models achieve self-evolution through interaction. Using LTM, our multi-agent framework OMNE achieved first place on the GAIA benchmark, demonstrating LTM's potential for AI self-evolution. Finally, we present a roadmap for future research, emphasizing the importance of LTM for advancing AI technology and its practical applications., Comment: 56 pages, 13 figures

The clinical diagnosis of most mental disorders primarily relies on the conversations between psychiatrist and patient. The creation of such diagnostic conversation datasets is promising to boost the AI mental healthcare community. However, directly collecting the conversations in real diagnosis scenarios is near impossible due to stringent privacy and ethical considerations. To address this issue, we seek to synthesize diagnostic conversation by exploiting anonymous patient cases that are easier to access. Specifically, we design a neuro-symbolic multi-agent framework for synthesizing the diagnostic conversation of mental disorders with large language models. It takes patient case as input and is capable of generating multiple diverse conversations with one single patient case. The framework basically involves the interaction between a doctor agent and a patient agent, and achieves text generation under symbolic control via a dynamic diagnosis tree from a tool agent. By applying the proposed framework, we develop the largest Chinese mental disorders diagnosis dataset MDD-5k, which is built upon 1000 cleaned real patient cases by cooperating with a pioneering psychiatric hospital, and contains 5000 high-quality long conversations with diagnosis results as labels. To the best of our knowledge, it's also the first labelled Chinese mental disorders diagnosis dataset. Human evaluation demonstrates the proposed MDD-5k dataset successfully simulates human-like diagnostic process of mental disorders. The dataset and code will become publicly accessible in https://github.com/lemonsis/MDD-5k.

The Comisso-Asenjo mechanism is a novel mechanism proposed recently to extract energy from black holes through magnetic reconnection of the surrounding charged plasma, in which the magnetic field plays a crucial role. In this work, we revisit this process by taking into account the backreaction of the magnetic field on the black hole's geometry. We employ the Kerr-Melvin metric to describe the local near-horizon geometry of the magnetized black hole. By analyzing the circular orbits in the equatorial plane, energy extraction conditions, power and efficiency of the energy extraction, we found that while a stronger magnetic field can enhance plasma magnetization and aid energy extraction, its backreaction on the spacetime may hinder the process, with a larger magnetic field posing a greater obstacle. Balancing these effects, an optimal moderate magnetic field strength is found to be most conducive to energy extraction. Moreover, there is a maximum limit to the magnetic field strength associated with the black hole's spin, beyond which circular orbits in the equatorial plane are prohibited, thereby impeding energy extraction in the current scenario., Comment: 19 pages, 7 figures, minor modifications to match published version. arXiv admin note: text overlap with arXiv:2402.15050

We study the phenomenon of spontaneous scalarization in parameterized Schwarzschild-like black holes. Two metrics are considered, the Konoplya-Zhidenko metric and the Johannsen-Psaltis metric. While these metrics can mimic the Schwarzschild black hole well in the weak-field regime, they have deformed geometries in the near-horizon strong-field region. Such deformations notably influence the emergence of tachyonic instability and subsequent spontaneous scalarization, enabling a clear distinction between these parameterized metrics and the standard Schwarzschild metric. These results suggest a possible way to test the parameterized black holes and thus the Kerr hypothesis by observing the phenomenon of spontaneous scalarization., Comment: 19 pages,10 figures,published version

In the realm of data-driven AI technology, the application of open-source large language models (LLMs) in robotic task planning represents a significant milestone. Recent robotic task planning methods based on open-source LLMs typically leverage vast task planning datasets to enhance models' planning abilities. While these methods show promise, they struggle with complex long-horizon tasks, which require comprehending more context and generating longer action sequences. This paper addresses this limitation by proposing MLDT, theMulti-Level Decomposition Task planning method. This method innovatively decomposes tasks at the goal-level, task-level, and action-level to mitigate the challenge of complex long-horizon tasks. In order to enhance open-source LLMs' planning abilities, we introduce a goal-sensitive corpus generation method to create high-quality training data and conduct instruction tuning on the generated corpus. Since the complexity of the existing datasets is not high enough, we construct a more challenging dataset, LongTasks, to specifically evaluate planning ability on complex long-horizon tasks. We evaluate our method using various LLMs on four datasets in VirtualHome. Our results demonstrate a significant performance enhancement in robotic task planning, showcasing MLDT's effectiveness in overcoming the limitations of existing methods based on open-source LLMs as well as its practicality in complex, real-world scenarios.

Recently, Comisso and Asenjo proposed a novel mechanism for harnessing energy from black holes through magnetic reconnection. Our study focuses on exploring the utilization of this mechanism on Konoplya-Rezzolla-Zhidenko (KRZ) parametrized black holes to assess the impact of deformation parameters on energy extraction. Among the various parameters, $\{\delta_1, \delta_2\}$ are identified as the most important factors affecting the physics under consideration. The influence of these two parameters on the ergoregion, circular geodesics in the equatorial plane, and energy extraction from the KRZ black holes through magnetic reconnection is carefully analyzed. Results indicate that deviations from the Kerr metric have a notable influence on the energy extraction process. Particularly, energy extraction is enhanced with more negative ${\delta_1}$ or more positive ${\delta_2}$ within the range constrained by current astronomical observations, resulting in significantly higher maximum power and efficiency compared to the Kerr model. Moreover, when ${\delta_2}$ is sufficiently negative, extracting energy through this mechanism becomes increasingly challenging, necessitating an exceptionally high black hole spin., Comment: 22 pages,7 figures, minor modifications, more refs added, published version

Quantum simulation of a monopole-spin hybrid system is performed on basis of a dipolar ultracold gas in a ladder lattice. The site-occupation states of the dipolar ladder lattice gas can spontaneously emulate both the monopole and spin excitations. The hopping of the atoms induces a particle conversion process between spin and monopole pairs, and the dipole-dipole interaction determines the spin-spin, spin-monopole and monopole-monopole interactions. The anisotropic nature of the dipole-dipole interaction allows hereby for a flexible engineering of the designed hybrid system, and for a significant tunability of the interaction strengths. As a result, we encounter a rich phase diagram, and specifically a self-assembled Coulomb phase arises, in which monopoles and spins coexist and are orderly arranged according to the local Gauss's law. The Coulomb phase hosts a zoo of different types of quasiparticles, and provides the possibility to simulate various phenomena in particle physics, such as a degenerate vacuum, particle decay and conversion processes. Our work provides a significant extension of the scope of quantum simulations based on the anisotropy of dipolar interactions., Comment: 11 pages,8 figures

In randomized clinical trials, adjusting for baseline covariates can improve credibility and efficiency for demonstrating and quantifying treatment effects. This article studies the augmented inverse propensity weighted (AIPW) estimator, which is a general form of covariate adjustment that uses linear, generalized linear, and non-parametric or machine learning models for the conditional mean of the response given covariates. Under covariate-adaptive randomization, we establish general theorems that show a complete picture of the asymptotic normality, {efficiency gain, and applicability of AIPW estimators}. In particular, we provide for the first time a rigorous theoretical justification of using machine learning methods with cross-fitting for dependent data under covariate-adaptive randomization. Based on the general theorems, we offer insights on the conditions for guaranteed efficiency gain and universal applicability {under different randomization schemes}, which also motivate a joint calibration strategy using some constructed covariates after applying AIPW. Our methods are implemented in the R package RobinCar.

During the diagnostic process, clinicians leverage multimodal information, such as chief complaints, medical images, and laboratory-test results. Deep-learning models for aiding diagnosis have yet to meet this requirement. Here we report a Transformer-based representation-learning model as a clinical diagnostic aid that processes multimodal input in a unified manner. Rather than learning modality-specific features, the model uses embedding layers to convert images and unstructured and structured text into visual tokens and text tokens, and bidirectional blocks with intramodal and intermodal attention to learn a holistic representation of radiographs, the unstructured chief complaint and clinical history, structured clinical information such as laboratory-test results and patient demographic information. The unified model outperformed an image-only model and non-unified multimodal diagnosis models in the identification of pulmonary diseases (by 12% and 9%, respectively) and in the prediction of adverse clinical outcomes in patients with COVID-19 (by 29% and 7%, respectively). Leveraging unified multimodal Transformer-based models may help streamline triage of patients and facilitate the clinical decision process., Comment: Accepted by Nature Biomedical Engineering

We study the freeze-out parameters in a QCD-assisted effective theory that accurately captures the quantum and in-medium effects of QCD at low energies. Functional renormalization group approach is implemented in our work to incorporate the non-perturbative quantum, thermal and density fluctuations. By analyzing the calculated baryon number susceptibility ratios $\chi_{2}^{B}/\chi_{1}^{B}$ and $\chi_{3}^{B}/\chi_{2}^{B}$, we determine the chemical freeze-out temperatures and baryon chemical potentials in cases of hard thermal or dense loop improved $\mu$-dependent glue potential and $\mu$-independent glue potential. We calculate the ${\chi_{4}^{B}}/{\chi_{2}^{B}}\, (\kappa \sigma^{2})$ and ${\chi_{6}^{B}}/{\chi_{2}^{B}}$ along the freeze-out line for both cases. It's found that $\kappa \sigma^{2}$ exhibits a nonmonotonic behavior in low collision energy region and approach to one for lower collision energy. ${\chi_{6}^{B}}/{\chi_{2}^{B}}$ shows a similar complicated behavior in our calculation., Comment: 14 pages, 11 figures, 7 tables

It is well-known that parity symmetry is broken in the weak interaction but conserved for Einstein's general relativity and Maxwell's electromagnetic theory. Nevertheless, parity symmetry could also be violated in the gravitational/electromagnetic sectors if a fundamental scalar field couples to the parity-violating gravitational/electromagnetic curvature terms. Such parity-violating terms, which flip signs under reversed spatial directions, can inevitably lead to a negative effective mass squared for the scalar field perturbations near nonspherically symmetric black holes and thus are expected to trigger tachyonic instability. As illustrative examples, we show that the scalar field coupled to gravitational/electromagnetic Chern-Simons terms near a Kerr-Newmann spacetime can develop tachyonic instabilities, leading to equilibrium scalar field configurations in certain parameter regions of black holes. This instability, which is an indication of the black hole scalarization process, can occur in a broad class of nonspherically symmetric black holes and parity-violating theories., Comment: 9 pages, 3 figures, 1 table

Based on the spectral decomposition through a code of PrepSpec, the light curves (spanning 6.5 years in the observed frame) of the broad-line Balmer decrement, i.e., the flux ratio of the broad \ha to the broad \hb line, are calculated for a sample of 44 Sloan Digital Sky Survey reverberation-mapped quasars ($z<0.53$). It is found that the logarithm of the mean broad-line Balmer decrement is 0.62 with a standard deviation of 0.15 dex. The relations between the mean Balmer decrement and the SMBH accretion properties (the luminosity, black hole mass, Eddington ratio, accretion rate) are investigated and no obvious correlations are found. It is found that there are 27 quasars ($61\%$) showing strong negative correlations between the Balmer decrement variance and the continuum variance, i.e., the Balmer decrement would be smaller with larger continuum flux. Assuming that the dust obscuration leads to the variance in the Balmer decrement and the continuum, an expected slope is $-1/3$, which is not consistent with most of measured slopes. Using the interpolated cross-correlation function, the time delays between the inverse Balmer decrement and the continuum are measured for 14 quasars with the maximum correlation coefficient larger the 0.6. It suggests that the size corresponding to the Balmer decrement lag extends from the BLR size to the torus size., Comment: 10 pages, 10 figures, Accepted for publication in MNRAS

In this work, we present a new type of scalar clouds supported by spherically symmetric horizonless compact objects in the scalar-Gauss-Bonnet theory. Unlike the previous spontaneous scalarization that is triggered by the tachyonic instability, our scalarization arises from a nonlinear instability that is non-spontaneous. We explore two types of boundary conditions for the scalar field at the surface of the compact objects and find an infinite countable set of scalar clouds characterized by the number of nodes for both cases. Our study demonstrates that boundary conditions have a significant impact on the formation of scalar clouds. Specifically, for the Dirichlet boundary condition, scalarization is more likely to occur for compact objects with medium radii and becomes harder for ultra-compact and large ones. Conversely, for the Robin boundary condition, scalarization is easier for more compact objects., Comment: 13 pages, 6 figures; v2: typos corrected, more refs added

To improve precision of estimation and power of testing hypothesis for an unconditional treatment effect in randomized clinical trials with binary outcomes, researchers and regulatory agencies recommend using g-computation as a reliable method of covariate adjustment. However, the practical application of g-computation is hindered by the lack of an explicit robust variance formula that can be used for different unconditional treatment effects of interest. To fill this gap, we provide explicit and robust variance estimators for g-computation estimators and demonstrate through simulations that the variance estimators can be reliably applied in practice.

In the framework of Einstein-Maxwell-scalar theory, we studied scalar field perturbations of Reissner-Nordstr\"{o}m-Melvin (RNM) black holes, which describes the RN black holes immersed in a uniform magnetic field. Due to the coupling to the Maxwell term, the scalar field acquires an effective mass whose square, in the presence of the magnetic field, will become negative somewhere outside the horizon for either sign of the coupling constant $\alpha$, thus triggering the tachyonic instability and leading to spontaneous scalarization when $\alpha$ is large enough. The magnetic field has significant influences on the waveforms and the onset of the instability, which differs for different sign of $\alpha$. Effects of the black hole charge on the instability are also studied., Comment: 16 pages, 15 figures. arXiv admin note: text overlap with arXiv:2209.02268