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We present NOrthern Extended Millimeter Array observations of CO(8-7), (9-8), and (10-9) lines, as well as the underlying continuum for two far-infrared luminous quasars: SDSS J2054-0005 at $\rm z=6.0389$ and SDSS J0129-0035 at $\rm z=5.7788$. Both quasars were previously detected in CO (2-1) and (6-5) transitions, making them candidates for studying the CO Spectral Line Energy Distribution (SLED) of quasars at $z \sim 6$. Utilizing the radiative transfer code CLOUDY, we fit the CO SLED with two heating mechanisms, including the photo-dissociation region (PDR) and X-ray-dominated region (XDR) for both objects. The CO SLEDs can be fitted by either a dense PDR component with an extremely strong far-ultraviolet radiation field (gas density $ n_{\rm H} \sim 10^6 \, \rm cm^{-3}$ and field strength $G_0 \gtrsim 10^6$) or a two-component model including a PDR and an XDR. However, the line ratios, including \tir and previous \cii and \ci measurements, argue against a very high PDR radiation field strength. Thus, the results prefer a PDR+XDR origin for the CO SLED. The excitation of the high-J CO lines in both objects is likely dominated by the central AGN. We then check the CO (9-8)-to-(6-5) line luminosity ratio $r_{96}$ for all $z \sim 6$ quasars with available CO SLEDs (seven in total) and find that there are no clear correlations between $r_{96}$ and both \fir and the AGN UV luminosities. This further demonstrates the complexity of the CO excitation powered by both the AGN and nuclear star formation in these young quasar host galaxies., Comment: 19 pages, 9 figures, accepted for publication in ApJ

Ensemble clustering aggregates multiple weak clusterings to achieve a more accurate and robust consensus result. The Co-Association matrix (CA matrix) based method is the mainstream ensemble clustering approach that constructs the similarity relationships between sample pairs according the weak clustering partitions to generate the final clustering result. However, the existing methods neglect that the quality of cluster is related to its size, i.e., a cluster with smaller size tends to higher accuracy. Moreover, they also do not consider the valuable dissimilarity information in the base clusterings which can reflect the varying importance of sample pairs that are completely disconnected. To this end, we propose the Similarity and Dissimilarity Guided Co-association matrix (SDGCA) to achieve ensemble clustering. First, we introduce normalized ensemble entropy to estimate the quality of each cluster, and construct a similarity matrix based on this estimation. Then, we employ the random walk to explore high-order proximity of base clusterings to construct a dissimilarity matrix. Finally, the adversarial relationship between the similarity matrix and the dissimilarity matrix is utilized to construct a promoted CA matrix for ensemble clustering. We compared our method with 13 state-of-the-art methods across 12 datasets, and the results demonstrated the superiority clustering ability and robustness of the proposed approach. The code is available at https://github.com/xuz2019/SDGCA.

In systems with unconventional Rashba bands, we propose that a finite-momentum pairing state can emerge without the need for an external magnetic field. We analyze the phase transition from a zero-momentum Bardeen-Cooper-Schrieffer (BCS) state to a finite-momentum pairing state using a microscopic interaction model. We demonstrate that the coexistence of zero-momentum and Larkin-Ovchinnikov (LO)-type pairings in different channels provides a well understanding of recent experimental observations of pair-density-wave (PDW) states. Furthermore, we propose that an s-wave BCS superconductor-unconventional Rashba metal (SC-URM) junction can generate an LO-type pairing state via an orbital-selective proximity effect. This nontrivial state can be detected by measuring the Josephson current in an SC-URM-SC junction or through Josephson scanning tunneling microscopy/spectrosscopy (JSTM/S). Our results reveal that the internal multi-orbital degrees of freedom play a crucial role in facilitating finite-momentum pairing states., Comment: 7 pages, 3 figures

We investigate the kinematics and dynamics of the molecular and ionized gas in the host galaxies of three Palomar-Green quasars at low redshifts, benefiting from the archival millimeter-wave interferometric and optical integral field unit data. We study the kinematics of both cold molecular and hot ionized gas by analyzing the CO and H$\alpha$ data cubes, and construct the mass distributions of our sample through gas dynamics, utilizing a priori knowledge regarding the galaxy light distribution. We find no systematic offset between the stellar mass derived from our dynamical method and that from the broad-band photometry and mass-to-light ratio, suggesting the consistency of both methods. We then study the kinetic pressure and the weight of the interstellar medium using our dynamical mass model. By studying the relationship between kinetic pressure and gravitational pressure of the quasar host galaxies, we find an equivalence in the hydrostatic equilibrium states of ISM in the quasar host galaxies, similar to the result of gas equilibrium in normal star-forming galaxies, suggesting minimal quasar feedback. Regarding non-circular motion as indicative of quasar-driven outflows, we observe an exceptionally low coupling efficiency between molecular gas outflow and AGN bolometric luminosities. These results demonstrate the marginal influence of the central engine on the properties of cold molecular gas in quasar host galaxies., Comment: 27 pages, 12 figures, accepted for publication in ApJ

Recent advancements in reinforcement learning have made significant impacts across various domains, yet they often struggle in complex multi-agent environments due to issues like algorithm instability, low sampling efficiency, and the challenges of exploration and dimensionality explosion. Hierarchical reinforcement learning (HRL) offers a structured approach to decompose complex tasks into simpler sub-tasks, which is promising for multi-agent settings. This paper advances the field by introducing a hierarchical architecture that autonomously generates effective subgoals without explicit constraints, enhancing both flexibility and stability in training. We propose a dynamic goal generation strategy that adapts based on environmental changes. This method significantly improves the adaptability and sample efficiency of the learning process. Furthermore, we address the critical issue of credit assignment in multi-agent systems by synergizing our hierarchical architecture with a modified QMIX network, thus improving overall strategy coordination and efficiency. Comparative experiments with mainstream reinforcement learning algorithms demonstrate the superior convergence speed and performance of our approach in both single-agent and multi-agent environments, confirming its effectiveness and flexibility in complex scenarios. Our code is open-sourced at: \url{https://github.com/SICC-Group/GMAH}.

High-redshift radio(-loud) galaxies (H$z$RGs) are massive galaxies with powerful radio-loud active galactic nuclei (AGNs) and serve as beacons for protocluster identification. However, the interplay between H$z$RGs and the large-scale environment remains unclear. To understand the connection between H$z$RGs and the surrounding obscured star formation, we investigated the overdensity and spatial distribution of submillimeter-bright galaxies (SMGs) in the field of 4C\,23.56, a well-known H$z$RG at $z=2.48$. We used SCUBA-2 data ($\sigma\,{\sim}\,0.6$\,mJy) to estimate the $850\,{\rm \mu m}$ source number counts and examine the radial and azimuthal overdensities of the $850\,{\rm \mu m}$ sources in the vicinity of the H$z$RG. The angular distribution of SMGs is inhomogeneous around the H$z$RG 4C\,23.56, with fewer sources oriented along the radio jet. We also find a significant overdensity of bright SMGs (${\rm S}_{850\rm\,\mu m}\geq5\,$mJy). Faint and bright SMGs exhibit different spatial distributions. The former are concentrated in the core region, while the latter prefer the outskirts of the H$z$RG field. High-resolution observations show that the seven brightest SMGs in our sample are intrinsically bright, suggesting that the overdensity of bright SMGs is less likely due to the source multiplicity., Comment: 19 pages, 17 figures, 5 tables, accepted to A&A

Online job advertisements on various job portals or websites have become the most popular way for people to find potential career opportunities nowadays. However, the majority of these job sites are limited to offering fundamental filters such as job titles, keywords, and compensation ranges. This often poses a challenge for job seekers in efficiently identifying relevant job advertisements that align with their unique skill sets amidst a vast sea of listings. Thus, we propose well-coordinated visualizations to provide job seekers with three levels of details of job information: a skill-job overview visualizes skill sets, employment posts as well as relationships between them with a hierarchical visualization design; a post exploration view leverages an augmented radar-chart glyph to represent job posts and further facilitates users' swift comprehension of the pertinent skills necessitated by respective positions; a post detail view lists the specifics of selected job posts for profound analysis and comparison. By using a real-world recruitment advertisement dataset collected from 51Job, one of the largest job websites in China, we conducted two case studies and user interviews to evaluate JobViz. The results demonstrated the usefulness and effectiveness of our approach.

This paper proposes a weight-aware deep reinforcement learning (WADRL) approach designed to address the multiobjective vehicle routing problem with time windows (MOVRPTW), aiming to use a single deep reinforcement learning (DRL) model to solve the entire multiobjective optimization problem. The Non-dominated sorting genetic algorithm-II (NSGA-II) method is then employed to optimize the outcomes produced by the WADRL, thereby mitigating the limitations of both approaches. Firstly, we design an MOVRPTW model to balance the minimization of travel cost and the maximization of customer satisfaction. Subsequently, we present a novel DRL framework that incorporates a transformer-based policy network. This network is composed of an encoder module, a weight embedding module where the weights of the objective functions are incorporated, and a decoder module. NSGA-II is then utilized to optimize the solutions generated by WADRL. Finally, extensive experimental results demonstrate that our method outperforms the existing and traditional methods. Due to the numerous constraints in VRPTW, generating initial solutions of the NSGA-II algorithm can be time-consuming. However, using solutions generated by the WADRL as initial solutions for NSGA-II significantly reduces the time required for generating initial solutions. Meanwhile, the NSGA-II algorithm can enhance the quality of solutions generated by WADRL, resulting in solutions with better scalability. Notably, the weight-aware strategy significantly reduces the training time of DRL while achieving better results, enabling a single DRL model to solve the entire multiobjective optimization problem., Comment: 13 pages; Under Review; Submitted to IEEE Transactions on Intelligent Transportation Systems

Let $\{X(t)\}_{t\geqslant0}$ be the generalized fractional Brownian motion introduced by Pang and Taqqu (2019): \begin{align*} \{X(t)\}_{t\ge0}\overset{d}{=}&\left\{ \int_{\mathbb R} \left((t-u)_+^{\alpha}-(-u)_+^{\alpha} \right) |u|^{-\gamma/2} B(du) \right\}_{t\ge0}, \end{align*} where $ \gamma\in [0,1), \ \ \alpha\in \left(-\frac12+\frac{\gamma}{2}, \ \frac12+\frac{\gamma}{2} \right)$ are constants. For any $\theta>0$, let \begin{align*} Y(t)=\frac{1}{\Gamma(\theta)}\int_0^t (t-u)^{\theta-1} X(u)du, \quad t\ge 0. \end{align*} Building upon the arguments of Talagrand (1996), we give integral criteria for the lower classes of $Y$ at $t=0$ and at infinity, respectively. As a consequence, we derive its Chung-type laws of the iterated logarithm. In the proofs, the small ball probability estimates play important roles., Comment: 19 papges, comments welcome

This work introduces a novel value decomposition algorithm, termed \textit{Dynamic Deep Factor Graphs} (DDFG). Unlike traditional coordination graphs, DDFG leverages factor graphs to articulate the decomposition of value functions, offering enhanced flexibility and adaptability to complex value function structures. Central to DDFG is a graph structure generation policy that innovatively generates factor graph structures on-the-fly, effectively addressing the dynamic collaboration requirements among agents. DDFG strikes an optimal balance between the computational overhead associated with aggregating value functions and the performance degradation inherent in their complete decomposition. Through the application of the max-sum algorithm, DDFG efficiently identifies optimal policies. We empirically validate DDFG's efficacy in complex scenarios, including higher-order predator-prey tasks and the StarCraft II Multi-agent Challenge (SMAC), thus underscoring its capability to surmount the limitations faced by existing value decomposition algorithms. DDFG emerges as a robust solution for MARL challenges that demand nuanced understanding and facilitation of dynamic agent collaboration. The implementation of DDFG is made publicly accessible, with the source code available at \url{https://github.com/SICC-Group/DDFG}., Comment: submitted to IEEE TPAMI

Semi-supervised symmetric non-negative matrix factorization (SNMF) utilizes the available supervisory information (usually in the form of pairwise constraints) to improve the clustering ability of SNMF. The previous methods introduce the pairwise constraints from the local perspective, i.e., they either directly refine the similarity matrix element-wisely or restrain the distance of the decomposed vectors in pairs according to the pairwise constraints, which overlook the global perspective, i.e., in the ideal case, the pairwise constraint matrix and the ideal similarity matrix possess the same low-rank structure. To this end, we first propose a novel semi-supervised SNMF model by seeking low-rank representation for the tensor synthesized by the pairwise constraint matrix and a similarity matrix obtained by the product of the embedding matrix and its transpose, which could strengthen those two matrices simultaneously from a global perspective. We then propose an enhanced SNMF model, making the embedding matrix tailored to the above tensor low-rank representation. We finally refine the similarity matrix by the strengthened pairwise constraints. We repeat the above steps to continuously boost the similarity matrix and pairwise constraint matrix, leading to a high-quality embedding matrix. Extensive experiments substantiate the superiority of our method. The code is available at https://github.com/JinaLeejnl/TSNMF.

The [CII] 158 $\mu$m emission line and the underlying far-infrared (FIR) dust continuum are important tracers for studying star formation and kinematic properties of early galaxies. We present a survey of the [CII] emission lines and FIR continua of 31 luminous quasars at $z>6.5$ using the Atacama Large Millimeter Array (ALMA) and the NOrthern Extended Millimeter Array (NOEMA) at sub-arcsec resolution. This survey more than doubles the number of quasars with [CII] and FIR observations at these redshifts and enables statistical studies of quasar host galaxies deep into the epoch of reionization. We detect [CII] emission in 27 quasar hosts with a luminosity range of $L_{\rm [CII]}=(0.3-5.5)\times10^9~L_\odot$ and detect the FIR continuum of 28 quasar hosts with a luminosity range of $L_{\rm FIR}=(0.5-13.0)\times10^{12}~L_\odot$. Both $L_{\rm [CII]}$ and $L_{\rm FIR}$ are correlated ($\rho\simeq0.4$) with the quasar bolometric luminosity, albeit with substantial scatter. The quasar hosts detected by ALMA are clearly resolved with a median diameter of $\sim$5 kpc. About 40% of the quasar host galaxies show a velocity gradient in [CII] emission, while the rest show either dispersion-dominated or disturbed kinematics. Basic estimates of the dynamical masses of the rotation-dominated host galaxies yield $M_{\rm dyn}=(0.1-7.5)\times10^{11}~M_\odot$. Considering our findings alongside those of literature studies, we found that the ratio between $M_{\rm BH}$ and $M_{\rm dyn}$ is about ten times higher than that of local $M_{\rm BH}-M_{\rm dyn}$ relation on average but with substantial scatter (the ratio difference ranging from $\sim$0.6 to 60) and large uncertainties., Comment: accepted for publication in ApJ

We present a statistical study of the properties of diffuse HI in ten nearby galaxies, comparing the HI detected by the single-dish telescope FAST (FEASTS program) and the interferometer VLA (THINGS program), respectively. The THINGS' observation missed HI with a median of 23% due to the short-spacing problem of interferometry and limited sensitivity. We extract the diffuse HI by subtracting the dense HI, which is obtained from the THINGS data with a uniform flux-density threshold, from the total HI detected by FAST. Among the sample, the median diffuse-HI fraction is 34%, and more diffuse HI is found in galaxies exhibiting more prominent tidal-interaction signatures. The diffuse HI we detected seems to be distributed in disk-like layers within a typical thickness of $1\,\text{kpc}$, different from the more halo-like diffuse HI detected around NGC 4631 in a previous study. Most of the diffuse HI is cospatial with the dense HI and has a typical column density of $10^{17.7}$-$10^{20.1}\,\text{cm}^{-2}$. The diffuse and dense HI exhibits a similar rotational motion, but the former lags by a median of 25% in at least the inner disks, and its velocity dispersions are typically twice as high. Based on a simplified estimation of circum-galactic medium properties and assuming pressure equilibrium, the volume density of diffuse HI appears to be constant within each individual galaxy, implying its role as a cooling interface. Comparing with existing models, these results are consistent with a possible link between tidal interactions, the formation of diffuse HI, and gas accretion., Comment: 45 pages, 23 figures. In press at ApJ. Data will be released at the FEASTS site upon publication

Machine learning potentials (MLPs) are widely applied as an efficient alternative way to represent potential energy surfaces (PES) in many chemical simulations. The MLPs are often evaluated with the root-mean-square errors on the test set drawn from the same distribution as the training data. Here, we systematically investigate the relationship between such test errors and the simulation accuracy with MLPs on an example of a full-dimensional, global PES for the glycine amino acid. Our results show that the errors in the test set do not unambiguously reflect the MLP performance in different simulation tasks such as relative conformer energies, barriers, vibrational levels, and zero-point vibrational energies. We also offer an easily accessible solution for improving the MLP quality in a simulation-oriented manner, yielding the most precise relative conformer energies and barriers. This solution also passed the stringent test by the diffusion Monte Carlo simulations.

In two-dimensional system with Rashba spin-orbit coupling, it is well-known that superconductivity can have mixed spin-singlet and -triplet parity, and the $\boldsymbol{d}$-vector of spin-triplet pairing is parallel to $\boldsymbol{g}$-vector of Rashba spin-orbit coupling. Here, we propose a model to describe a two-dimensional system with unconventional Rashba bands and study its superconductivity. We show that the $\boldsymbol{d}$-vector of spin-triplet pairing can be either parallel or perpendicular to $\boldsymbol{g}$-vector of Rashba spin-orbit coupling depending on the different pairing interaction. We also propose a junction to generate tunneling current depending on the direction of $\boldsymbol{d}$-vector. It provides a detectable evidence to distinguish these two different but very similar pairing channels. Furthermore, we find this model can give arise to a subleading spin-singlet chiral $p$-wave topological superconducting state. More significantly, we find that such unconventional Rashba bands and unconventional superconudcting pairings can be realized on surface of some superconducting topological materials, such as trigonal layered PtBi$_{2}$., Comment: 14 pages, 3 figures

Electrocatalytic CO2 reduction technology is key to mitigating greenhouse gas emissions and the energy crisis. However, controlling the selectivity of CO2RR products at low overpotential remains a challenge. In this paper, we predicted five high-performance CO2RR electrocatalysts with different product-specific selectivities at the theoretical level based on the advantages of the compositional structure and the tunable pore size of 2D {\pi}-conjugated MOFs. In addition, through the reaction mechanism and electronic structure analysis, we found that the synergistic interaction between metal atoms and organic linkers of 2D MOFs can effectively regulate the electronic structure of the active center. Their pore size as well as the diversity of carbon materials can regulate the spin magnetic moments of the metal atoms, thus affecting the improvement of their catalytic performance. Meanwhile, the oxygen or carbon affinity of the catalyst surface determines the differences in the formation of key intermediates, which ultimately determines the reaction path and product selectivity. These insights we present will be useful for the development and design of highly active CO2RR electrocatalysts.

We present observations using the NOrthern Extended Millimetre Array (NOEMA) of CO and $\rm H_{2}O$ emission lines, and the underlying dust continuum in two quasars at $z \sim 6$, i.e., P215-16 at $z$ = 5.78 and J1429+5447 at $z$ = 6.18. Notably, among all published CO SLEDs of quasars at $z \sim 6$, the two systems reveal the highest and the lowest CO level of excitation, respectively. Our radiative transfer modeling of the CO SLED of P215-16 suggests that the molecular gas heated by AGN could be a plausible origin for the high CO excitation. For J1429+5447, we obtain the first well-sampled CO SLED (from transitions from 2-1 to 10-9) of a radio-loud quasar at $z\gtrsim 6$. Analysis of the CO SLED suggests that a single photo-dissociation region (PDR) component could explain the CO excitation in the radio-loud quasar J1429+5447. This work highlights the utility of the CO SLED in uncovering the ISM properties in these young quasar-starburst systems at the highest redshift. The diversity of the CO SLEDs reveals the complexities in gas conditions and excitation mechanisms at their early evolutionary stage., Comment: Accepted for publication in Apj

Disentangling orbital (OAM) and spin (SAM) angular momenta in the ultrafast spin dynamics of two-dimensional (2D) ferromagnets on subfemtoseconds is a challenge in the field of ultrafast magnetism. Herein, we employed non-collinear spin version of real-time time-dependent density functional theory to investigate the orbital and spin dynamics of 2D ferromagnets Fe3GeTe2 (FGT) induced by circularly polarized light. Our results show the demagnetization of Fe sublattice in FGT is accompanied by helicity-dependent precession of OAM and SAM excited by circularly polarized lasers. We further identify that precession of OAM and SAM in FGT is faster than the demagnetization within a few femtoseconds. Remarkably, circularly polarized lasers can significantly induce a periodically transverse response of OAM and SAM on very ultrafast timescales of ~250 attoseconds. Our finding suggests a powerful new route for attosecond regimes of the angular momentum manipulation to coherently control helicity-dependent orbital and spin dynamics in 2D limits.

The Cholinergic Pathways Hyperintensities Scale (CHIPS) is a visual rating scale used to assess the extent of cholinergic white matter hyperintensities in T2-FLAIR images, serving as an indicator of dementia severity. However, the manual selection of four specific slices for rating throughout the entire brain is a time-consuming process. Our goal was to develop a deep learning-based model capable of automatically identifying the four slices relevant to CHIPS. To achieve this, we trained a 4-class slice classification model (BSCA) using the ADNI T2-FLAIR dataset (N=150) with the assistance of ResNet. Subsequently, we tested the model's performance on a local dataset (N=30). The results demonstrated the efficacy of our model, with an accuracy of 99.82% and an F1-score of 99.83%. This achievement highlights the potential impact of BSCA as an automatic screening tool, streamlining the selection of four specific T2-FLAIR slices that encompass white matter landmarks along the cholinergic pathways. Clinicians can leverage this tool to assess the risk of clinical dementia development efficiently., Comment: 8 pages, 2 figures, 2 tables

This paper studies the learning-to-control problem under process and sensing uncertainties for dynamical systems. In our previous work, we developed a data-based generalization of the iterative linear quadratic regulator (iLQR) to design closed-loop feedback control for high-dimensional dynamical systems with partial state observation. This method required perfect simulation rollouts which are not realistic in real applications. In this work, we briefly introduce this method and explore its efficacy under process and sensing uncertainties. We prove that in the fully observed case where the system dynamics are corrupted with noise but the measurements are perfect, it still converges to the global minimum. However, in the partially observed case where both process and measurement noise exist in the system, this method converges to a biased "optimum". Thus multiple rollouts need to be averaged to retrieve the true optimum. The analysis is verified in two nonlinear robotic examples simulated in the above cases.

Recent advances in machine learning have made revolutionary breakthroughs in computer games, image and natural language understanding, and scientific discovery. Foundation models and large-scale language models (LLMs) have recently achieved human-like intelligence thanks to BigData. With the help of self-supervised learning (SSL) and transfer learning, these models may potentially reshape the landscapes of neuroscience research and make a significant impact on the future. Here we present a mini-review on recent advances in foundation models and generative AI models as well as their applications in neuroscience, including natural language and speech, semantic memory, brain-machine interfaces (BMIs), and data augmentation. We argue that this paradigm-shift framework will open new avenues for many neuroscience research directions and discuss the accompanying challenges and opportunities.

Large-scale pre-trained language models (LLMs) have demonstrated exceptional performance in various natural language processing (NLP) tasks. However, the massive size of these models poses huge challenges for their deployment in real-world applications. While numerous model compression techniques have been proposed, most of them are not well-suited for achieving extreme model compression when there is a significant gap in model scale. In this paper, we introduce a novel compression paradigm called Retrieval-based Knowledge Transfer (RetriKT), which effectively transfers the knowledge of LLMs to extremely small-scale models (e.g., 1%). In particular, our approach extracts knowledge from LLMs to construct a knowledge store, from which the small-scale model can retrieve relevant information and leverage it for effective inference. To improve the quality of the model, soft prompt tuning and Proximal Policy Optimization (PPO) reinforcement learning techniques are employed. Extensive experiments are conducted on low-resource tasks from SuperGLUE and GLUE benchmarks. The results demonstrate that the proposed approach significantly enhances the performance of small-scale models by leveraging the knowledge from LLMs., Comment: EMNLP 2023 Findings

We report the first statistical analyses of [CII] and dust continuum observations in six strong OI absorber fields at the end of the reionization epoch obtained by the Atacama Large Millimeter/Submillimeter Array (ALMA). Combined with one [CII] emitter reported in Wu et al. (2021), we detect one OI-associated [CII] emitter in six fields. At redshifts of OI-absorbers in non-detection fields, no emitters are brighter than our detection limit within impact parameters of 50 kpc and velocity offsets between $\pm200\ {\rm km\ s^{-1}}$. The averaged [CII]-detection upper limit is $< 0.06$ Jy ${\rm km\ s^{-1}}$ (3$\sigma$), corresponding to the [CII] luminosity of $L_{\rm [CII]} <5.8\times 10^7\ L_{\odot}$ and the [CII]-based star formation rate of ${\rm SFR_{\rm [CII]}} < 5.5$ $M_\odot$ yr$^{-1}$. Cosmological simulations suggest that only $\sim10^{-2.5}$ [CII] emitters around [OI] absorbers have comparable SFR to our detection limit. Although the detection in one out of six fields is reported, an order of magnitude number excess of emitters obtained from our ALMA observations supports that the contribution of massive galaxies that caused the metal enrichment cannot be ignored. Further, we also found 14 tentative galaxy candidates with S/N of $\approx4.3$ at large impact parameters ($>50$ kpc) and having larger outflow velocities within $\pm 600$ km s$^{-1}$. If these detections are confirmed in the future, then the mechanism of pushing metals at larger distances with higher velocities needs to be further explored from the theoretical side., Comment: Accepted for publication in ApJ. Main text 10 pages, 5 figures

In our modern, fast-paced, and interconnected world, the importance of mental well-being has grown into a matter of great urgency. However, traditional methods such as Emotional Support Conversations (ESC) face challenges in effectively addressing a diverse range of individual personalities. In response, we introduce the Social Support Conversation (S2Conv) framework. It comprises a series of support agents and the interpersonal matching mechanism, linking individuals with persona-compatible virtual supporters. Utilizing persona decomposition based on the MBTI (Myers-Briggs Type Indicator), we have created the MBTI-1024 Bank, a group that of virtual characters with distinct profiles. Through improved role-playing prompts with behavior preset and dynamic memory, we facilitate the development of the MBTI-S2Conv dataset, which contains conversations between the characters in the MBTI-1024 Bank. Building upon these foundations, we present CharacterChat, a comprehensive S2Conv system, which includes a conversational model driven by personas and memories, along with an interpersonal matching plugin model that dispatches the optimal supporters from the MBTI-1024 Bank for individuals with specific personas. Empirical results indicate the remarkable efficacy of CharacterChat in providing personalized social support and highlight the substantial advantages derived from interpersonal matching. The source code is available in \url{https://github.com/morecry/CharacterChat}., Comment: 10 pages, 6 figures, 5 tables

Due to shortage of water resources and increasing water demands, the joint operation of multireservoir systems for balancing power generation, ecological protection, and the residential water supply has become a critical issue in hydropower management. However, the numerous constraints and nonlinearity of multiple reservoirs make solving this problem time-consuming. To address this challenge, a deep reinforcement learning approach that incorporates a transformer framework is proposed. The multihead attention mechanism of the encoder effectively extracts information from reservoirs and residential areas, and the multireservoir attention network of the decoder generates suitable operational decisions. The proposed method is applied to Lake Mead and Lake Powell in the Colorado River Basin. The experimental results demonstrate that the transformer-based deep reinforcement learning approach can produce appropriate operational outcomes. Compared to a state-of-the-art method, the operation strategies produced by the proposed approach generate 10.11% more electricity, reduce the amended annual proportional flow deviation by 39.69%, and increase water supply revenue by 4.10%. Consequently, the proposed approach offers an effective method for the multiobjective operation of multihydropower reservoir systems., Comment: 11 figures, 16 pages, submitted to Journal of Hydrology