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The Transition from Galaxy-wide Gas Inflow to Outflow in Quasar Host Galaxies
No full textGalactic-wide outflows driven by active galactic nuclei (AGNs) is a routinely invoked feedback mechanism in galaxy evolution models. Hitherto, the interplay among the interstellar gas on galactic scales, the propagation of AGN outflows and the fundamental AGN parameters during evolution remains elusive. Powerful nuclear outflows are found to favorably exist at early AGN stages usually associated with high accretion rates and weak narrow emission lines. In a sample of quasars emitting Mg II narrow absorption lines (NALs) from the Sloan Digital Sky Survey, we discover an unprecedented phenomenon where galaxy-scale inflow-dominated transforming into outflow-dominated gas accompanied by an increasing strength of the narrow [O III] line, at a confidence level of 6.7σ. The fact that nuclear outflows diminish while galaxy-wide outflows intensifies as AGNs evolve implies that early-stage outflows interact with interstellar medium on galactic scales and trigger the gradual transformation into galaxy-wide outflows, providing observational links to the hypothetical multi-stage propagation of AGN outflows that globally regulates galaxy evolution.Accepted by SCIENCE CHINA Physics, Mechanics & Astronomy; 15 pages, 4 figures and 6 appendix figure
Power Modeling in mm-Wave and Terahertz CMOS Transmitters for Wireless Network-on-Chip
No full textWireless Network-on-Chip (WNoC) systems, which interconnect chips using wireless links, face significant challenges in area and power consumption. To tackle these constraints, behavioral models (BMs) are crucial for assessing system performance under various conditions and optimizing parameters like data throughput and power consumption. Building transceivers (TRXs) physically is costly and time-consuming, making modeling a more practical approach. This paper develops a power consumption model for the sub-blocks of a WNoC transmitter (TX) at the chip level. By integrating these BMs with MATLAB, we aim to create a power model for TXs in WNoC architectures, optimized for CMOS technology operating at millimeter-wave and terahertz frequencies
Ideals, quotients, and continuity of the Cuntz semigroup for rings
No full textIn this paper we explore which part of the ideal lattice of a general ring is parametrized by its Cuntz semigroup and its ambient semigroup . We identify these classes of ideals as the quasipure ideals (a generalization of pure ideals) in the case of , and what we term decomposable ideals in the case of . For an (-)unital ring , the latter class exhausts all ideals of the ring. We prove that these constructions behave well with respect to quotients. In order to study the passage to inductive limits, we introduce the classes of dense and left normal rings. We show that is an abstract Cu-semigroup whenever is left normal and, for such rings, the assignment is continuous. We prove a parallel result for whenever is a dense ring.38 pages. Comments welcome
Tactical Edge IoT in Defense and National Security
No full textThe deployment of Internet of Things (IoT) systems in Defense and National Security faces some limitations that can be addressed with Edge Computing approaches. The Edge Computing and IoT paradigms combined bring potential benefits, since they confront the limitations of traditional centralized cloud computing approaches, which enable easy scalability, real-time applications or mobility support, but whose use poses certain risks in aspects like cybersecurity. This chapter identifies scenarios in which Defense and National Security can leverage Commercial Off-The-Shelf (COTS) Edge IoT capabilities to deliver greater survivability to warfighters or first responders, while lowering costs and increasing operational efficiency and effectiveness. In addition, it presents the general design of a Tactical Edge IoT communications architecture, it identifies the open challenges for a widespread adoption and provides research guidelines and some recommendations for enabling cost-effective Edge IoT for Defense and National Security.Book chapter accepted in the book IoT for Defense and National Security (Wiley-IEEE Press
Tumor Location-weighted MRI-Report Contrastive Learning: A Framework for Improving the Explainability of Pediatric Brain Tumor Diagnosis
No full textDespite the promising performance of convolutional neural networks (CNNs) in brain tumor diagnosis from magnetic resonance imaging (MRI), their integration into the clinical workflow has been limited. That is mainly due to the fact that the features contributing to a model\u27s prediction are unclear to radiologists and hence, clinically irrelevant, i.e., lack of explainability. As the invaluable sources of radiologists\u27 knowledge and expertise, radiology reports can be integrated with MRI in a contrastive learning (CL) framework, enabling learning from image-report associations, to improve CNN explainability. In this work, we train a multimodal CL architecture on 3D brain MRI scans and radiology reports to learn informative MRI representations. Furthermore, we integrate tumor location, salient to several brain tumor analysis tasks, into this framework to improve its generalizability. We then apply the learnt image representations to improve explainability and performance of genetic marker classification of pediatric Low-grade Glioma, the most prevalent brain tumor in children, as a downstream task. Our results indicate a Dice score of 31.1% between the model\u27s attention maps and manual tumor segmentation (as an explainability measure) with test classification performance of 87.7%, significantly outperforming the baselines. These enhancements can build trust in our model among radiologists, facilitating its integration into clinical practices for more efficient tumor diagnosis
Generative AI and Agency in Education: A Critical Scoping Review and Thematic Analysis
No full textThis scoping review examines the relationship between Generative AI (GenAI) and agency in education, analyzing the literature available through the lens of Critical Digital Pedagogy. Following PRISMA-ScR guidelines, we collected 10 studies from academic databases focusing on both learner and teacher agency in GenAI-enabled environments. We conducted an AI-supported hybrid thematic analysis that revealed three key themes: Control in Digital Spaces, Variable Engagement and Access, and Changing Notions of Agency.
The findings suggest that while GenAI may enhance learner agency through personalization and support, it also risks exacerbating educational inequalities and diminishing learner autonomy in certain contexts. This review highlights gaps in the current research on GenAI\u27s impact on agency. These findings have implications for educational policy and practice, suggesting the need for frameworks that promote equitable access while preserving learner agency in GenAI-enhanced educational environments
Cascading Dark Energy
No full textThe standard cosmological model is in the midst of a stress test, thanks to the tension between supernovae-based measurements of the Hubble constant and inferences of its values from Cosmic Microwave Background (CMB) anisotropies. Numerous explanations for the present-day cosmic acceleration require the presence of a new fundamental scalar field, as do Early Dark Energy (EDE) solutions to the Hubble tension. This raises the possibility that multiple fields cooperatively contribute to the dark energy component in bursts throughout cosmic time due to distinct initial conditions and couplings. Here, this Cascading Dark Energy (CDE) scenario is illustrated through a realization that effectively reduces to a two-field model, with two epochs in which dark energy is cosmologically significant. The model is compared to measurements of the CMB, baryon acoustic oscillations, as well as both PANTHEON and SH0ES observations of Type-Ia supernovae. Neglecting the linear perturbations, it is found that this scenario ameliorates the Hubble tension, improving over purely late-time models of dark energy, and the agreement between the galaxy survey measurements of baryon acoustic oscillations.v1: 13 pages, 7 figures, double-column; v2: 18 pages, 9 figures, double-colum
DeepLag: Discovering Deep Lagrangian Dynamics for Intuitive Fluid Prediction
No full textAccurately predicting the future fluid is vital to extensive areas such as meteorology, oceanology, and aerodynamics. However, since the fluid is usually observed from the Eulerian perspective, its moving and intricate dynamics are seriously obscured and confounded in static grids, bringing thorny challenges to the prediction. This paper introduces a new Lagrangian-Eulerian combined paradigm to tackle the tanglesome fluid dynamics. Instead of solely predicting the future based on Eulerian observations, we propose DeepLag to discover hidden Lagrangian dynamics within the fluid by tracking the movements of adaptively sampled key particles. Further, DeepLag presents a new paradigm for fluid prediction, where the Lagrangian movement of the tracked particles is inferred from Eulerian observations, and their accumulated Lagrangian dynamics information is incorporated into global Eulerian evolving features to guide future prediction respectively. Tracking key particles not only provides a transparent and interpretable clue for fluid dynamics but also makes our model free from modeling complex correlations among massive grids for better efficiency. Experimentally, DeepLag excels in three challenging fluid prediction tasks covering 2D and 3D, simulated and real-world fluids. Code is available at this repository: https://github.com/thuml/DeepLag
DDIPrompt: Drug-Drug Interaction Event Prediction based on Graph Prompt Learning
No full textDrug combinations can cause adverse drug-drug interactions(DDIs). Identifying specific effects is crucial for developing safer therapies. Previous works on DDI event prediction have typically been limited to using labels of specific events as supervision, which renders them insufficient to address two significant challenges: (1) the bias caused by \textbf{highly imbalanced event distribution} where certain interaction types are vastly under-represented. (2) the \textbf{scarcity of labeled data for rare events}, a pervasive issue where rare yet potentially critical interactions are often overlooked or under-explored due to limited available data. In response, we offer ``DDIPrompt\u27\u27, an innovative solution inspired by the recent advancements in graph prompt learning. Our framework aims to address these issues by leveraging the intrinsic knowledge from pre-trained models, which can be efficiently deployed with minimal downstream data. Specifically, to solve the first challenge, DDIPrompt features a hierarchical pre-training strategy to foster a generalized and comprehensive understanding of drug properties. It captures intra-molecular structures through augmented links based on structural proximity between drugs, further learns inter-molecular interactions emphasizing edge connections rather than concrete catagories. For the second challenge, we implement a prototype-enhanced prompting mechanism during inference. This mechanism, refined by few-shot examples from each category, effectively harnesses the rich pre-training knowledge to enhance prediction accuracy, particularly for these rare but crucial interactions. Extensive experiments on two benchmark datasets demonstrate DDIPrompt\u27s SOTA performance, especially for those rare DDI events
Vision-Aware Text Features in Referring Image Segmentation: From Object Understanding to Context Understanding
No full textReferring image segmentation is a challenging task that involves generating pixel-wise segmentation masks based on natural language descriptions. The complexity of this task increases with the intricacy of the sentences provided. Existing methods have relied mostly on visual features to generate the segmentation masks while treating text features as supporting components. However, this under-utilization of text understanding limits the model\u27s capability to fully comprehend the given expressions. In this work, we propose a novel framework that specifically emphasizes object and context comprehension inspired by human cognitive processes through Vision-Aware Text Features. Firstly, we introduce a CLIP Prior module to localize the main object of interest and embed the object heatmap into the query initialization process. Secondly, we propose a combination of two components: Contextual Multimodal Decoder and Meaning Consistency Constraint, to further enhance the coherent and consistent interpretation of language cues with the contextual understanding obtained from the image. Our method achieves significant performance improvements on three benchmark datasets RefCOCO, RefCOCO+ and G-Ref. Project page: \url{https://vatex.hkustvgd.com/}.This paper is accepted in WACV 202