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Powerful, galactic outflows driven by Active Galactic Nuclei (AGNs) are commonly considered as a main mechanism to regulate star formation in massive galaxies. Ultra- and hyper-luminous IR galaxies (U/HyLIRGs) are thought to represent a transition phase of galaxies from a rapidly growing period to a quiescent status as gas swept out by outflows, providing a laboratory to investigate outflows and their feedback effects on the hosts. In this paper we report recent Gemini and ALMA observations of a HyLIRG, J1126 at $z=0.46842$, which has been identified with a puzzling co-existence of a fast ionized outflow ($>2000$ km s$^{-1}$) and an intense starburst (star formation rate of 800 $M_{\odot}$ yr$^{-1}$). The Gemini observation shows the fast ionized outflow is extended to several kpc with a mass-loss rate of 180 $M_{\odot}$ yr$^{-1}$. A massive molecular outflow with a high mass-loss rate (2500 $M_{\odot}$ yr$^{-1}$) is revealed by ALMA. The multi-phase outflows show large factors of momentum boost and loading of kinetic power, indicating a driving by thermal pressure of a nuclear hot wind and/or radiation pressure of a highly obscured AGN. In addition to ejection of kinetic energy, it is also found that the powerful outflow can induce an ionizing shock in the galaxy disk and enhance the excitation and dissociation of molecular gas. The powerful outflow probably results in an instantaneous negative feedback and shows potential to regulate the host growth in a long term., Comment: 35 pages, 26 figures, accepted to be published in ApJ

The discovery of interface-enhanced superconductivity in single-layer FeSe/oxides has generated intensive research interests. Beyond the family of FeSe interfaced with various TiO$_2$ terminated oxides, high pairing temperature up to 80~K has been recently observed in FeSe interfaced with FeO$_x$-terminated LaFeO$_3$. Here we successfully extend the FeSe/FeO$_x$ superconducting interface to FeSe/NdFeO$_3$, by constructing 1uc-FeSe/6uc-NdFeO$_3$/Nb:SrTiO$_3$ heterostructures. Intriguingly, well-annealed FeSe/NdFeO$_3$ exhibits a low doping level of 0.038$\sim$0.046 ~e$^-/$Fe which deviates universally magic doping level (0.10$\sim$0.12 e$^-/\rm{Fe}$) and provides a new playground for studying the FeSe/oxide interface in the low electron-doped regime. Comparing it with thick FeSe films at the comparable electron doping level induced by surface potassium dosing, FeSe/NdFeO$_3$ shows a larger superconducting gap and the absence of a nematic gap, indicating an enhancement of the superconductivity and suppression of nematicity by the FeSe/FeO$_x$ interface. These results not only expand the FeSe/FeO$_x$ superconducting family but also enrich the current understanding on the roles of the oxide interface., Comment: 8 pages, 4 figures

Monolayer FeSe/TiO$_x$ and FeSe/FeO$_x$ interfaces exhibit significant superconductivity enhancement compared to bulk FeSe, with interfacial electron-phonon coupling (EPC) playing a crucial role. However, the reduced dimensionality in monolayer FeSe, which may drive superconducting fluctuations, complicates the understanding of the enhancement mechanisms. Here we construct a new superconducting interface: monolayer FeSe/SrVO$_3$/SrTiO$_3$, in which the itinerant electrons of highly metallic SrVO$_3$ films can screen all the high-energy Fuchs-Kliewer phonons, including those of SrTiO$_3$, making it the first FeSe/oxide system with screened interfacial EPC while maintaining the monolayer FeSe thickness. Despite comparable doping levels, the heavily electron-doped monolayer FeSe/SrVO$_3$ exhibits a lower pairing temperature ($T_\mathrm{g}$ $\sim$ 48 K) than FeSe/SrTiO$_3$ and FeSe/LaFeO$_3$. Our findings disentangle the contributions of interfacial EPC from dimensionality on enhancing $T_\mathrm{g}$ in FeSe/oxide interfaces, underscoring the importance of interfacial EPC in $T_\mathrm{g}$ enhancement. This FeSe/VO$_x$ interface also provides a platform for studying the interfacial superconductivity., Comment: Published in Nano Letters, 11 pages, 4 figures, 1 table

We present Atacama Large Millimeter/submillimeter Array observations of the [C II] 158 $\mu$m line and the underlying continuum emission of TN J0924$-$2201, which is one of the most distant known radio galaxies at $z>5$. The [C II] line and 1-mm continuum emission are detected at the host galaxy. The systemic redshift derived from the [C II] line is $z_{\rm [C II]}=5.1736\pm0.0002$, indicating that the Ly$\alpha$ line is redshifted by a velocity of $1035\pm10$ km s$^{-1}$, marking the largest velocity offset between the [C II] and Ly$\alpha$ lines recorded at $z>5$ to date. In the central region of the host galaxy, we identified a redshifted substructure of [C II] with a velocity of $702\pm17$ km s$^{-1}$, which is close to the CIV line with a velocity of $500\pm10$ km s$^{-1}$. The position and the velocity offsets align with a model of an outflowing shell structure, consistent with the large velocity offset of Ly$\alpha$. The non-detection of [C II] and dust emission from the three CO(1--0)-detected companions indicates their different nature compared to dwarf galaxies based on the photodissociation region model. Given their large velocity of $\sim1500$ km s$^{-1}$, outflowing molecular clouds induced by the AGN is the most plausible interpretation, and they may exceed the escape velocity of a $10^{13}\,M_{\odot}$ halo. These results suggest that TN J0924$-$2201, with the ongoing and fossil large-scale outflows, is in a distinctive phase of removing molecular gas from a central massive galaxy in an overdense region in the early universe. A dusty HI absorber at the host galaxy is an alternative interpretation., Comment: 16 pages, 11 figures, accepted for publication in ApJ

Entity matching (EM) is a critical step in entity resolution (ER). Recently, entity matching based on large language models (LLMs) has shown great promise. However, current LLM-based entity matching approaches typically follow a binary matching paradigm that ignores the global consistency between record relationships. In this paper, we investigate various methodologies for LLM-based entity matching that incorporate record interactions from different perspectives. Specifically, we comprehensively compare three representative strategies: matching, comparing, and selecting, and analyze their respective advantages and challenges in diverse scenarios. Based on our findings, we further design a compound entity matching framework (ComEM) that leverages the composition of multiple strategies and LLMs. ComEM benefits from the advantages of different sides and achieves improvements in both effectiveness and efficiency. Experimental results on 8 ER datasets and 9 LLMs verify the superiority of incorporating record interactions through the selecting strategy, as well as the further cost-effectiveness brought by ComEM., Comment: Code is available at https://github.com/tshu-w/LLM4EM

Current methods for developing foundation models in medical image segmentation rely on two primary assumptions: a fixed set of classes and the immediate availability of a substantial and diverse training dataset. However, this can be impractical due to the evolving nature of imaging technology and patient demographics, as well as labor-intensive data curation, limiting their practical applicability and scalability. To address these challenges, we introduce a novel segmentation paradigm enabling the segmentation of a variable number of classes within a single classifier-free network, featuring an architecture independent of class number. This network is trained using contrastive learning and produces discriminative feature representations that facilitate straightforward interpretation. Additionally, we integrate this strategy into a knowledge distillation-based incremental learning framework, facilitating the gradual assimilation of new information from non-stationary data streams while avoiding catastrophic forgetting. Our approach provides a unified solution for tackling both class- and domain-incremental learning scenarios. We demonstrate the flexibility of our method in handling varying class numbers within a unified network and its capacity for incremental learning. Experimental results on an incompletely annotated, multi-modal, multi-source dataset for medical image segmentation underscore its superiority over state-of-the-art alternative approaches.

Blocking is a critical step in entity resolution, and the emergence of neural network-based representation models has led to the development of dense blocking as a promising approach for exploring deep semantics in blocking. However, previous advanced self-supervised dense blocking approaches require domain-specific training on the target domain, which limits the benefits and rapid adaptation of these methods. To address this issue, we propose UniBlocker, a dense blocker that is pre-trained on a domain-independent, easily-obtainable tabular corpus using self-supervised contrastive learning. By conducting domain-independent pre-training, UniBlocker can be adapted to various downstream blocking scenarios without requiring domain-specific fine-tuning. To evaluate the universality of our entity blocker, we also construct a new benchmark covering a wide range of blocking tasks from multiple domains and scenarios. Our experiments show that the proposed UniBlocker, without any domain-specific learning, significantly outperforms previous self- and unsupervised dense blocking methods and is comparable and complementary to the state-of-the-art sparse blocking methods., Comment: Code and data are available at this https://github.com/tshu-w/Uniblocker

The practice of Retrieval-Augmented Generation (RAG), which integrates Large Language Models (LLMs) with retrieval systems, has become increasingly prevalent. However, the repercussions of LLM-derived content infiltrating the web and influencing the retrieval-generation feedback loop are largely uncharted territories. In this study, we construct and iteratively run a simulation pipeline to deeply investigate the short-term and long-term effects of LLM text on RAG systems. Taking the trending Open Domain Question Answering (ODQA) task as a point of entry, our findings reveal a potential digital "Spiral of Silence" effect, with LLM-generated text consistently outperforming human-authored content in search rankings, thereby diminishing the presence and impact of human contributions online. This trend risks creating an imbalanced information ecosystem, where the unchecked proliferation of erroneous LLM-generated content may result in the marginalization of accurate information. We urge the academic community to take heed of this potential issue, ensuring a diverse and authentic digital information landscape., Comment: Accepted to ACL2024

The striking discovery of high-temperature superconductivity (HTSC) of 80 K in a bilayer nickelate La$_3$Ni$_2$O$_7$ under a moderately high pressure of about 14 GPa ignited a new wave of studying HTSC in nickelates. The properties of the parental phase at ambient pressure may contain key information on basic interactions therein and bosons that may mediate pairing giving birth to superconductivity. Moreover, the bilayer structure of La$_3$Ni$_2$O$_7$ may suggest a distinct minimal model in comparison to cuprate superconductors. Here using X-ray absorption spectroscopy and resonant inelastic X-ray scattering, we studied La$_3$Ni$_2$O$_7$ at ambient pressure, and found that Ni 3$d_{x^2-y^2}$, Ni 3$d_{z^2}$, and ligand oxygen 2$p$ orbitals dominate the low-energy physics with a small charge-transfer energy. Remarkably, well-defined optical-like magnetic excitations were found to soften into a quasi-static spin-density-wave ordering, evidencing the strong electronic correlations and rich magnetic properties. Based on a Heisenberg spin model, we found that the inter-layer effective magnetic superexchange interaction is much larger than the intra-layer ones, and proposed two viable magnetic structures. Our results set the foundation for further exploration of La$_3$Ni$_2$O$_7$ superconductor.

Cortical surface reconstruction (CSR) from MRI is key to investigating brain structure and function. While recent deep learning approaches have significantly improved the speed of CSR, a substantial amount of runtime is still needed to map the cortex to a topologically-correct spherical manifold to facilitate downstream geometric analyses. Moreover, this mapping is possible only if the topology of the surface mesh is homotopic to a sphere. Here, we present a method for simultaneous CSR and spherical mapping efficiently within seconds. Our approach seamlessly connects two sub-networks for white and pial surface generation. Residual diffeomorphic deformations are learned iteratively to gradually warp a spherical template mesh to the white and pial surfaces while preserving mesh topology and uniformity. The one-to-one vertex correspondence between the template sphere and the cortical surfaces allows easy and direct mapping of geometric features like convexity and curvature to the sphere for visualization and downstream processing. We demonstrate the efficacy of our approach on infant brain MRI, which poses significant challenges to CSR due to tissue contrast changes associated with rapid brain development during the first postnatal year. Performance evaluation based on a dataset of infants from 0 to 12 months demonstrates that our method substantially enhances mesh regularity and reduces geometric errors, outperforming state-of-the-art deep learning approaches, all while maintaining high computational efficiency.

Text-to-SQL simplifies database interactions by enabling non-experts to convert their natural language (NL) questions into Structured Query Language (SQL) queries. While recent advances in large language models (LLMs) have improved the zero-shot text-to-SQL paradigm, existing methods face scalability challenges when dealing with massive, dynamically changing databases. This paper introduces DBCopilot, a framework that addresses these challenges by employing a compact and flexible copilot model for routing across massive databases. Specifically, DBCopilot decouples the text-to-SQL process into schema routing and SQL generation, leveraging a lightweight sequence-to-sequence neural network-based router to formulate database connections and navigate natural language questions through databases and tables. The routed schemas and questions are then fed into LLMs for efficient SQL generation. Furthermore, DBCopilot also introduced a reverse schema-to-question generation paradigm, which can learn and adapt the router over massive databases automatically without requiring manual intervention. Experimental results demonstrate that DBCopilot is a scalable and effective solution for real-world text-to-SQL tasks, providing a significant advancement in handling large-scale schemas., Comment: Code and data are available at https://github.com/tshu-w/DBCopilot

A versatile medical image segmentation model applicable to images acquired with diverse equipment and protocols can facilitate model deployment and maintenance. However, building such a model typically demands a large, diverse, and fully annotated dataset, which is challenging to obtain due to the labor-intensive nature of data curation. To address this challenge, we propose a cost-effective alternative that harnesses multi-source data with only partial or sparse segmentation labels for training, substantially reducing the cost of developing a versatile model. We devise strategies for model self-disambiguation, prior knowledge incorporation, and imbalance mitigation to tackle challenges associated with inconsistently labeled multi-source data, including label ambiguity and modality, dataset, and class imbalances. Experimental results on a multi-modal dataset compiled from eight different sources for abdominal structure segmentation have demonstrated the effectiveness and superior performance of our method compared to state-of-the-art alternative approaches. We anticipate that its cost-saving features, which optimize the utilization of existing annotated data and reduce annotation efforts for new data, will have a significant impact in the field., Comment: 14 pages, 5 figures

Dense retrieval has made significant advancements in information retrieval (IR) by achieving high levels of effectiveness while maintaining online efficiency during a single-pass retrieval process. However, the application of pseudo relevance feedback (PRF) to further enhance retrieval effectiveness results in a doubling of online latency. To address this challenge, this paper presents a single-pass dense retrieval framework that shifts the PRF process offline through the utilization of pre-generated pseudo-queries. As a result, online retrieval is reduced to a single matching with the pseudo-queries, hence providing faster online retrieval. The effectiveness of the proposed approach is evaluated on the standard TREC DL and HARD datasets, and the results demonstrate its promise. Our code is openly available at https://github.com/Rosenberg37/OPRF., Comment: Accepted at SIGIR2023

The Differentiable Search Index (DSI) is a novel information retrieval (IR) framework that utilizes a differentiable function to generate a sorted list of document identifiers in response to a given query. However, due to the black-box nature of the end-to-end neural architecture, it remains to be understood to what extent DSI possesses the basic indexing and retrieval abilities. To mitigate this gap, in this study, we define and examine three important abilities that a functioning IR framework should possess, namely, exclusivity, completeness, and relevance ordering. Our analytical experimentation shows that while DSI demonstrates proficiency in memorizing the unidirectional mapping from pseudo queries to document identifiers, it falls short in distinguishing relevant documents from random ones, thereby negatively impacting its retrieval effectiveness. To address this issue, we propose a multi-task distillation approach to enhance the retrieval quality without altering the structure of the model and successfully endow it with improved indexing abilities. Through experiments conducted on various datasets, we demonstrate that our proposed method outperforms previous DSI baselines., Comment: Accepted to Findings of ACL 2023

Recent high-$z$ quasar observations strongly indicate that super-Eddington accretion is a crucial phase to describe the existence of supermassive black holes (SMBHs) with $M_\mathrm{BH} \gtrsim 10^9 M_\odot$ at $z \gtrsim 7$. Motivated by the theoretical suggestion that the super-Eddington phase efficiently produces outflows and jets bright in radio bands, we search and find a super-Eddington radio-loud dust-obscured galaxy (DOG) J1406+0102 at $z=0.236$, through cross-matching of the infrared-bright DOGs of Noboriguchi et al. (2019) with the VLA/FIRST 1.4 GHz radio and the SDSS optical spectral catalog. DOG J1406+0102 shows broad components in the Balmer lines. Assuming those lines are from the broad line region, it gives BH mass estimation of $\log\ (M_\mathrm{BH}/M_\odot)=7.30 \pm 0.25$, and AGN luminosity of $\log (L_\mathrm{bol,[OIII]}/\mathrm{erg}~\mathrm{s}^{-1}) = 45.91\pm0.38$ estimated from the intrinsic [OIII] luminosity, resulting in super-Eddington accretion of $\lambda_\mathrm{Edd}\simeq 3$. We show that 1) DOG J1406+0102 is operating strong AGN feedback: the [OIII] outflow velocity exceeds the escape velocity of the host galaxy halo and the kinetic efficiency is obtained as $\approx$ 8% that can be sufficient to quench the host galaxy, 2) the expected future growth pathway of DOG J1406+0102 would join an over-massive BH trajectory and 3) radio-loud DOGs can provide a significant contribution to the high-energy ($\gtrsim$ 100 TeV) cosmic neutrino background if we assume DOG J1406+0102 as a representative of radio-loud DOGs., Comment: 10 pages, 5 figures, submitted to ApJL

We construct the eROSITA X-ray catalog of radio galaxies discovered by the WERGS survey that is made by the cross-matching of the wide-area Subaru/HSC optical survey and VLA/FIRST 1.4 GHz radio survey. We find 393 eROSITA detected radio galaxies in the 0.5--2 keV band in the eFEDS field covering 140~deg$^2$. Thanks to the wide and medium depth eFEDS X-ray survey, the sample contains the rare and most X-ray luminous radio galaxies above the knee of the X-ray luminosity function, spanning 4422$. Those obscured and radio AGN reside in $0.4

The emergent superconductivity at the LaAlO3/KTaO3 interfaces exhibits a mysterious dependence on the KTaO3 crystallographic orientations. Here we show, by soft X-ray angle-resolved photoemission spectroscopy, that the interfacial superconductivity is contributed by mobile electrons with unexpected quasi-three-dimensional character, beyond the "two-dimensional electron gas" scenario in describing oxide interfaces. At differently-oriented interfaces, the quasi-three-dimensional electron gas ubiquitously exists and spatially overlaps with the small q Fuchs-Kliewer surface phonons. Intriguingly, electrons and the Fuchs-Kliewer phonons couple with different strengths depending on the interfacial orientations, and the stronger coupling correlates with the higher superconducting transition temperature. Our results provide a natural explanation for the orientation-dependent superconductivity, and the first evidence that interfacial orientations can affect electron-phonon coupling strength over several nanometers, which may have profound implications for the applications of oxide interfaces in general.

Automatic segmentation of brain MR images into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) is critical for tissue volumetric analysis and cortical surface reconstruction. Due to dramatic structural and appearance changes associated with developmental and aging processes, existing brain tissue segmentation methods are only viable for specific age groups. Consequently, methods developed for one age group may fail for another. In this paper, we make the first attempt to segment brain tissues across the entire human lifespan (0-100 years of age) using a unified deep learning model. To overcome the challenges related to structural variability underpinned by biological processes, intensity inhomogeneity, motion artifacts, scanner-induced differences, and acquisition protocols, we propose to use contrastive learning to improve the quality of feature representations in a latent space for effective lifespan tissue segmentation. We compared our approach with commonly used segmentation methods on a large-scale dataset of 2,464 MR images. Experimental results show that our model accurately segments brain tissues across the lifespan and outperforms existing methods.

We derive several vanishing theorems for genera under almost nonnegative Ricci curvature and infinite fundamental group, which includes Todd genus, $\widehat{A}$-genus, elliptic genera and Witten genus. A vanishing theorem of Euler characteristic number for almost nonnegatively curved Alexandrov spaces is also proved.

While large-scale pre-trained language models like BERT have advanced the state-of-the-art in IR, its application in query performance prediction (QPP) is so far based on pointwise modeling of individual queries. Meanwhile, recent studies suggest that the cross-attention modeling of a group of documents can effectively boost performances for both learning-to-rank algorithms and BERT-based re-ranking. To this end, a BERT-based groupwise QPP model is proposed, in which the ranking contexts of a list of queries are jointly modeled to predict the relative performance of individual queries. Extensive experiments on three standard TREC collections showcase effectiveness of our approach. Our code is available at https://github.com/VerdureChen/Group-QPP., Comment: Accepted at Proceedings of the 44th European Conference on Information Retrieval, ECIR 2022

In transition-metal dipnictides $TmPn_2$ ($Tm$ = Ta, Nb; $Pn$ = P, As, Sb), the origin of extremely large magnetoresistance (XMR) is yet to be studied by the direct visualization of the experimental band structures. Here, using angle-resolved photoemission spectroscopy, we map out the three-dimensional electronic structure of NbAs$_2$. The open-orbit topology contributes to a non-negligible part of the Fermi surfaces (FSs), like that of the isostructural compound MoAs$_2$, where the open FS is proposed to likely explain the origin of XMR. We further demonstrate the observation of open characters in the overall FSs of W$_2$As$_3$, which is also a XMR semimetal with the same space group of $C$12/$m$1 as $TmPn_2$ family and MoAs$_2$. Our results suggest that the open-orbit FS topology may be a shared feature between XMR materials with the space group of $C$12/$m$1, and thus could possibly play a role in determining the corresponding XMR effect together with the electron-hole compensation., Comment: 7 pages, 4 figures, Editor's pick