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Mother-child interaction is a highly dynamic process neurally characterized by inter-brain synchrony (IBS) at {\theta} and/or {\alpha} rhythms. However, their establishment, dynamic changes, and roles in mother-child interactions remain unknown. Through dynamic analysis of dual-EEG from 40 mother-child dyads during turn-taking cooperation, we uncover that {\theta}-IBS and {\alpha}-IBS alternated with interactive behaviors, with EEG frequency-shift as a prerequisite for IBS transitions. When mothers attempt to track their children's attention and/or predict their intentions, they will adjust their EEG frequencies to align with their children's {\theta} oscillations, leading to a higher occurrence of the {\theta}-IBS state. Conversely, the {\alpha}-IBS state, accompanied by the EEG frequency-shift to the {\alpha} range, is more prominent during mother-led interactions. Further exploratory analysis reveals greater presence and stability of the {\theta}-IBS state during cooperative than non-cooperative conditions, particularly in dyads with stronger emotional attachments and more frequent interactions in their daily lives. Our findings shed light on the neural oscillational substrates underlying the IBS dynamics during mother-child interactions., Comment: 27 Pages,6 figures

Large Language Models(LLMs) excel in general tasks but struggle in specialized domains like healthcare due to limited domain-specific knowledge.Supervised Fine-Tuning(SFT) data construction for domain adaptation often relies on heuristic methods, such as GPT-4 annotation or manual data selection, with a data-centric focus on presumed diverse, high-quality datasets. However, these methods overlook the model's inherent knowledge distribution, introducing noise, redundancy, and irrelevant data, leading to a mismatch between the selected data and the model's learning task, resulting in suboptimal performance. To address this, we propose a two-stage model-centric data selection framework, Decomposed Difficulty Data Selection (3DS), which aligns data with the model's knowledge distribution for optimized adaptation. In Stage1, we apply Prompt-Driven Data Selection via Explicit Alignment, where the the model filters irrelevant or redundant data based on its internal knowledge. In Stage2, we perform Decomposed Difficulty Data Selection, where data selection is guided by our defined difficulty decomposition, using three metrics: Instruction Understanding, Response Confidence, and Response Correctness. Additionally, an attention-based importance weighting mechanism captures token importance for more accurate difficulty calibration. This two-stage approach ensures the selected data is not only aligned with the model's knowledge and preferences but also appropriately challenging for the model to learn, leading to more effective and targeted domain adaptation. In the case study of the medical domain, our extensive experiments on real-world healthcare datasets demonstrate the superiority of 3DS over exisiting methods in accuracy by over 5.29%. Our dataset and code will be open-sourced at https://anonymous.4open.science/r/3DS-E67F.

In the pursuit of enhancing domain-specific Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) emerges as a promising solution to mitigate issues such as hallucinations, outdated knowledge, and limited expertise in highly specialized queries. However, existing approaches to RAG fall short by neglecting system state variables, which are crucial for ensuring adaptive control, retrieval halting, and system convergence. In this paper, we introduce the TC-RAG through rigorous proof, a novel framework that addresses these challenges by incorporating a Turing Complete System to manage state variables, thereby enabling more efficient and accurate knowledge retrieval. By leveraging a memory stack system with adaptive retrieval, reasoning, and planning capabilities, TC-RAG not only ensures the controlled halting of retrieval processes but also mitigates the accumulation of erroneous knowledge via Push and Pop actions. In the case study of the medical domain, our extensive experiments on real-world healthcare datasets demonstrate the superiority of TC-RAG over existing methods in accuracy by over 7.20\%. Our dataset and code have been available at https://https://github.com/Artessay/SAMA.git., Comment: version 1.0

We employed a machine-learning assisted approach to search for superconducting hydrides under ambient pressure within an extensive dataset comprising over 150 000 compounds. Our investigation yielded around 50 systems with transition temperatures surpassing 20 K, and some even reaching above 70 K. These compounds have very different crystal structures, with different dimensionality, chemical composition, stoichiometry, and arrangement of the hydrogens. Interestingly, most of these systems displayed slight thermodynamic instability, implying that their synthesis would require conditions beyond ambient equilibrium. Moreover, we found a consistent chemical composition in the majority of these systems, which combines alkali or alkali-earth elements with noble metals. This observation suggests a promising avenue for future experimental investigations into high-temperature superconductivity within hydrides at ambient pressure.

Hyperbolic phonon polariton is important in precisely controlling photons at the nanoscale. It was common practice to calculate the dielectric function of the phonon polariton system with the Drude-Lorenz model. We considered the impact of LO-TO splitting while applying the Drude-Lorenz model. Then the dielectric functions become wave vector direction dependent besides electric polarization direction dependent. Our results show that considering LO-TO splitting can more accurately predict dielectric functions. Additionally, we discovered that, besides hexagonal BN, hexagonal AlN exhibits a wide hyperbolic frequency band range, while the other four materials display it scarcely. Furthermore, we found that the phonon frequency, lifetime, and the difference of infrared active transverse optical phonon frequencies with different wave vector directions are critical factors in determining the width of the hyperbolic frequency band range. We also found some dumbbell-shaped and butterfly-shaped isofrequency curves in h-AlN, h-GaP, and especially h-GaN. Our study provides a fresh perspective on understanding the dielectric properties of these materials and lays a theoretical foundation for further exploration and development of new hyperbolic phonon polariton materials.

The capacity to externally manipulate magnetic properties is highly desired from both fundamental and technological perspectives, particularly in the development of magnetoelectronics and spintronics devices. Here, using first-principles calculations, we have demonstrated the ability of controlling the magnetism of magnetized graphene monolayers by interfacing them with a two-dimensional ferroelectric material. When the 3$d$ transition metal (TM) is adsorbed on the graphene monolayer, its magnetization easy axis can be flipped from in-plane to out-of-plane by the ferroelectric polarization reversal of In$_2$Se$_3$, and the magnetocrystalline anisotropy energy (MAE) can be high to -0.692 meV/atom when adopting the Fe atom at bridge site with downward polarization. This may be a universal method since the 3$d$ TM-adsorbed graphene has a very small MAE, which can be easily manipulated by the ferroelectric polarization. As a result, the inherent mechanism is analyzed by second variation method., Comment: 17 pages, 10 figures in main text

This paper studies policy learning under the condition of unconfoundedness with a continuous treatment variable. Our research begins by employing kernel-based inverse propensity-weighted (IPW) methods to estimate policy welfare. We aim to approximate the optimal policy within a global policy class characterized by infinite Vapnik-Chervonenkis (VC) dimension. This is achieved through the utilization of a sequence of sieve policy classes, each with finite VC dimension. Preliminary analysis reveals that welfare regret comprises of three components: global welfare deficiency, variance, and bias. This leads to the necessity of simultaneously selecting the optimal bandwidth for estimation and the optimal policy class for welfare approximation. To tackle this challenge, we introduce a semi-data-driven strategy that employs penalization techniques. This approach yields oracle inequalities that adeptly balance the three components of welfare regret without prior knowledge of the welfare deficiency. By utilizing precise maximal and concentration inequalities, we derive sharper regret bounds than those currently available in the literature. In instances where the propensity score is unknown, we adopt the doubly robust (DR) moment condition tailored to the continuous treatment setting. In alignment with the binary-treatment case, the DR welfare regret closely parallels the IPW welfare regret, given the fast convergence of nuisance estimators.

This paper proposes a Heaviside composite optimization approach and presents a progressive (mixed) integer programming (PIP) method for solving multi-class classification and multi-action treatment problems with constraints. A Heaviside composite function is a composite of a Heaviside function (i.e., the indicator function of either the open $( \, 0,\infty )$ or closed $[ \, 0,\infty \, )$ interval) with a possibly nondifferentiable function. Modeling-wise, we show how Heaviside composite optimization provides a unified formulation for learning the optimal multi-class classification and multi-action treatment rules, subject to rule-dependent constraints stipulating a variety of domain restrictions. A Heaviside composite function has an equivalent discrete formulation, and the resulting optimization problem can in principle be solved by integer programming (IP) methods. Nevertheless, for constrained learning problems with large data sets, a straightforward application of off-the-shelf IP solvers is usually ineffective in achieving global optimality. To alleviate such a computational burden, our major contribution is the proposal of the PIP method by leveraging the effectiveness of state-of-the-art IP solvers for problems of modest sizes. We provide the theoretical advantage of the PIP method with the connection to continuous optimization and show that the computed solution is locally optimal for a broad class of Heaviside composite optimization problems. The numerical performance of the PIP method is demonstrated by extensive computational experimentation.

In this paper, we investigate the retrieval-augmented generation (RAG) based on Knowledge Graphs (KGs) to improve the accuracy and reliability of Large Language Models (LLMs). Recent approaches suffer from insufficient and repetitive knowledge retrieval, tedious and time-consuming query parsing, and monotonous knowledge utilization. To this end, we develop a Hypothesis Knowledge Graph Enhanced (HyKGE) framework, which leverages LLMs' powerful reasoning capacity to compensate for the incompleteness of user queries, optimizes the interaction process with LLMs, and provides diverse retrieved knowledge. Specifically, HyKGE explores the zero-shot capability and the rich knowledge of LLMs with Hypothesis Outputs to extend feasible exploration directions in the KGs, as well as the carefully curated prompt to enhance the density and efficiency of LLMs' responses. Furthermore, we introduce the HO Fragment Granularity-aware Rerank Module to filter out noise while ensuring the balance between diversity and relevance in retrieved knowledge. Experiments on two Chinese medical multiple-choice question datasets and one Chinese open-domain medical Q&A dataset with two LLM turbos demonstrate the superiority of HyKGE in terms of accuracy and explainability., Comment: version 2

The primary challenge in the field of high-temperature superconductivity in hydrides is to achieve a superconducting state at ambient pressure rather than the extreme pressures that have been required in experiments so far. Here, we propose a family of compounds, of composition Mg$_2$XH$_6$ with X$=$Rh, Ir, Pd, or Pt, that achieves this goal. These materials were identified by scrutinizing more than a million compounds using a machine-learning accelerated high-throughput workflow. They are thermodynamically stable, indicating that they are serious candidates for experimental synthesis. We predict that their superconducting transition temperatures are in the range of 45-80K, or even above 100K with appropriate electron doping of the Pt compound. These results indicate that, although very rare, high-temperature superconductivity in thermodynamically stable hydrides is achievable at room pressure., Comment: 9 pages, 6 figures

The recent report of near-ambient superconductivity in nitrogen-doped lutetium hydrides (Lu-N-H) has generated a great interest. However, conflicting results have raised doubts regarding superconductivity. Here, we combine high-throughput crystal structure predictions with a fast predictor of the superconducting critical temperature ($T_c$) to shed light on the properties of Lu-N-H at 1 GPa. None of the predicted structures shows the potential to support high-temperature superconductivity and the inclusion of nitrogen favors the appearance of insulating phases. Despite the lack of near-ambient superconductivity, we consider alternative metastable templates and study their $T_c$ and dynamical stability including quantum anharmonic effects. The cubic Lu$_4$H$_{11}$N exhibits a high $T_c$ of 100 K at 20 GPa, a large increase compared to 30 K obtained in its parent LuH$_3$. Interestingly, it has a similar X-ray pattern to the experimentally observed one. The LaH$_{10}$-like LuH$_{10}$ and CaH$_6$-like LuH$_6$ become high-temperature superconductors at 175 GPa and 100 GPa, with $T_c$ of 286 K and 246 K, respectively. Our findings suggest that high-temperature superconductivity is not possible in stable phases at near-ambient pressure, but metastable high-$T_c$ templates exist at moderate and high pressures., Comment: 52 pages, 5 figures and 1 table in main text, 11 figures and 2 tables in Supplementary

Rapidly increasing interest in low-dimensional materials is driven by the emerging requirement to develop nanoscale solid-state devices with novel functional properties that are not available in three-dimensional bulk phases. Among the well-known low-dimensional systems, complex transition metal oxide interface holds promise for broad applications in electronic and spintronics devices. Herein, intriguing metal-insulator and ferromagnetic-antiferromagnetic transitions are achieved in monolayer MnO$_2$ that is sandwiched into SrTiO$_3$-based heterointerface systems through interface engineering. By using first-principles calculations, we modeled three types of SrTiO$_3$-based heterointerface systems with different interface terminations and performed a comparative study on the spin-dependent magnetic and electronic properties that are established in the confined MnO$_2$ monolayer. First-principles study predicts that metal-insulator transition and magnetic transition in the monolayer MnO$_2$ are independent on the thickness of capping layers. Moreover, 100$\%$ spin-polarized two-dimensional electron gases accompanied by robust room temperature magnetism are uncovered in the monolayer MnO$_2$. Not only is the buried MnO$_2$ monolayer a new interface phase of fundamental physical interest, but it is also a promising candidate material for nanoscale spintronics applications. Our study suggests interface engineering at complex oxide interfaces is an alternative approach to designing high-performance two-dimensional materials., Comment: 24 pages, 7 figures

The recent report of near-ambient conditions superconductivity in a nitrogen-doped lutetium hydride has inspired a large number of experimental studies with contradictory results. We model from first principles the physical properties of the possible parent structures of the reported superconductor, LuH$_2$ and LuH$_3$. We show that only the phonon band structure of LuH$_3$ can explain the reported Raman spectra due to the presence of hydrogens at the interstitial octahedral sites. However, this structure is stabilized by anharmonicity only above 6 GPa. We find that the intriguing color change with pressure in the reported superconductor is consistent with the optical properties of LuH$_2$, which are determined by the presence of an undamped interband plasmon. The plasmon blue-shifts with pressure and modifies the color of the sample without requiring any structural phase transition. Our findings suggest that the main component in the experiments is LuH$_2$ with some extra hydrogen atoms at octahedral sites. None of LuH$_2$ and LuH$_3$ superconduct at high temperatures.

Using first-principles calculations we examine the crystal structures and phase transitions of nitride perovskite LaWN$_3$. Lattice dynamics calculations indicate that the ground-state structure belongs to space group $R3c$. Two competitive phase transition pathways are identified which are characterized by symmetry-adapted distortion modes. The results suggest that $R3c$ LaWN$_3$ should be an excellent ferroelectric semiconductor: its large spontaneous polarization of around 61 $\mu$C/cm$^2$ is comparable to that of PbTiO$_3$, and its band gap is about 1.72 eV. Ferroelectricity is found to result from the \emph{B}-site instability driven by hybridization between W-5$d$ and N-2$p$ orbitals. These properties make LaWN$_3$ an attractive candidate material for use in ferroelectric memory devices and photovoltaic cells., Comment: 13 pages, 8 figures in main text and 5 figures in supplementary

Quantum coherence is the basic concept of superposition of quantum states and plays an important role in quantum metrology. We show how a pair of uniformly accelerated observers with a local two-mode Gaussian quantum state affects the Gaussian quantum coherence. We find that the quantum coherence decreases with increasing acceleration, which is due to the Unruh effect that destroys the quantum resource. Essentially, the variation of quantum coherence is caused by the modes mismatch between the input and output mode. Through 2000 randomly generated states, we demonstrate that such mismatch is dominated by the acceleration effect and mildly affected by the waveform parameters. Moreover, the squeezing parameter acted as a suppressor of the reduced coherence, but it tended to be invalid in the high squeezing. In addition, the squeezing parameter can act as a suppressor of the reduced coherence, but the effect of the squeezing parameter tends to be ineffective under high squeezing conditions., Comment: 7 pages, 5 figures, accepted for publication in The European Physical Journal Plus

Mother-child interaction is highly dynamic and reciprocal. Switching roles in these back-and-forth interactions serves as a crucial feature of reciprocal behaviors while the underlying neural entrainment is still not well-studied. Here, we designed a role-controlled cooperative task with dual EEG recording to study how differently two brains interact when mothers and children hold different roles. When children were actors and mothers were observers, mother-child inter-brain synchrony emerged within the theta oscillations and the frontal lobe, which highly correlated with children's attachment to their mothers. When their roles were reversed, this synchrony was shifted to the alpha oscillations and the central area and associated with mothers' perception of their relationship with their children. The results suggested an observer-actor neural alignment within the actor's oscillations, which was modulated by the actor-toward-observer emotional bonding. Our findings contribute to the understanding of how inter-brain synchrony is established and dynamically changed during mother-child reciprocal interaction.

Helium, the second most abundant element in the universe, exhibits an extremely large electronic band gap of about $20$ eV at low pressures ($\le 0.1$ GPa). While the metallization pressure of hcp helium has been accurately predicted, thus far little attention has been paid to the specific mechanisms driving the band-gap closure and electronic properties of this quantum crystal in the terapascal regime (1 TPa $= 1,000$ GPa). Here, we employ state-of-the-art density functional theory and many-body perturbation theory calculations to fill up this knowledge gap. It is found that prior to reaching metallicity bulk solid helium becomes an excitonic insulator (EI), an exotic state of matter typically observed in low-dimensional systems in which electrostatically bound electron-hole pairs form spontaneously. Furthermore, it is shown that electron-phonon coupling (EPC) is significantly enhanced across the EI to metal phase transition as signaled by prominent phonon softening and giant EPC strength values ($\lambda \sim 10-100$) estimated at specific reciprocal space points. Accordingly, we predict metallic helium to be a superconductor with a critical temperature of $\approx 30$ K at $20$ TPa and of $\approx 100$ K at $100$ TPa. These unforeseen phenomena have important consequences on the elastic, thermodynamic and transport properties of metallic helium hence may be critical for improving our fundamental understanding and modelling of celestial bodies., Comment: 25 pages, 18 figures

BL Lac object PKS 0735+178 showed some complex multi-wavelength variation phenomena in the previous studies, especially for its color behavior. Bluer-when-brighter, redder-when-brighter and achromatic behavior were all found to be possible long-term trends of PKS 0735+178. In this work, we collected the long-term multi-wavelength data of PKS 0735+178, and also performed a multi-color optical monitoring on intraday timescale. The intraday variability was detected on one night. On the long timescale, a possible 22-day time lag was found between the $R$ and $\gamma$-ray bands. The results of the cross-correlation analysis exhibited strong correlations between various optical bands on both intraday and long timescale. However, only a mild correlation was found between the long-term $\gamma$-ray and $R$-band light curves, which could be interpreted by the different emission mechanisms of the $\gamma$-ray and optical emissions. PKS 0735+178 showed a significant harder-when-brighter in the $\gamma$-ray band, which is consistent with the observed optical bluer-when-brighter trend on both long-term and intraday timescales. We found that the HWB and BWB trends will be enhanced during the active states, especially for the historical low state. Such phenomenon indicates a special activity-dependent color behavior of PKS 0735+178, and it could be well interpreted by the jet emission model., Comment: Accepted for publication in ApJ, 19 pages, 13 figures

Measuring the quasar distance through joint analysis of spectroastrometry (SA) and reverberation mapping (RM) observations is a new method for driving the development of cosmology. In this paper, we carry out detailed simulation and analysis to study the effect of four basic observational parameters (baseline length, exposure time, equivalent diameter and spectral resolution) on the data quality of differential phase curves (DPCs), furthermore on the accuracy of distance measurement. In our simulation, we adopt an axis symmetrical disc model of broad line region (BLR) to generate differential phase signals. We find that the differential phases and their Poisson errors could be amplified by extending the baseline, while the influence of OPD (optical path difference) errors can be reduced during fitting the BLR model. Longer exposure time or larger equivalent diameter helps reduce the absolute Poisson error. Therefore, the relative error of DPCs could be reduce by increasing any of the above three parameters, then the accuracy of distance measurement could be improved. In contrast, the uncertainty of $D_{\rm{A}}$ ( absolute angular distances) could be improved with higher spectral resolution, although the relative error of DPCs would be amplified. We show how the uncertainty of distance measurement varies with the relative error of DPCs. For our specific set of model parameters, without considering more complicated structures and kinematics of BLRs in our simulation, it is found that the relative error of DPCs $<$ 20$\%$ is a limit for accurate distance measurement. The relative error of DPCs have a lower limit (roughly 5$\%$) and the uncertainty of distance measurement can be better than 2$\%$.

In this paper, we propose a Spatial Robust Mixture Regression model to investigate the relationship between a response variable and a set of explanatory variables over the spatial domain, assuming that the relationships may exhibit complex spatially dynamic patterns that cannot be captured by constant regression coefficients. Our method integrates the robust finite mixture Gaussian regression model with spatial constraints, to simultaneously handle the spatial nonstationarity, local homogeneity, and outlier contaminations. Compared with existing spatial regression models, our proposed model assumes the existence a few distinct regression models that are estimated based on observations that exhibit similar response-predictor relationships. As such, the proposed model not only accounts for nonstationarity in the spatial trend, but also clusters observations into a few distinct and homogenous groups. This provides an advantage on interpretation with a few stationary sub-processes identified that capture the predominant relationships between response and predictor variables. Moreover, the proposed method incorporates robust procedures to handle contaminations from both regression outliers and spatial outliers. By doing so, we robustly segment the spatial domain into distinct local regions with similar regression coefficients, and sporadic locations that are purely outliers. Rigorous statistical hypothesis testing procedure has been designed to test the significance of such segmentation. Experimental results on many synthetic and real-world datasets demonstrate the robustness, accuracy, and effectiveness of our proposed method, compared with other robust finite mixture regression, spatial regression and spatial segmentation methods., Comment: Accepted by ICDM IEEE 2021