Ask or search anything...
Strong spectral features from asymptotic giant branch stars in distant quiescent galaxies
03 Nov 2024
CNRS
University of CambridgeDating the ages and weighting the stellar populations in galaxies are essential steps when studying galaxy formation through cosmic times. Evolutionary population synthesis models with different input physics are used for this purpose. Moreover, the contribution from the thermally pulsing asymptotic giant branch (TP-AGB) stellar phase, which peaks for intermediate-age 0.6-2 Gyr, has been debated for decades. Here we report the detection of strong cool-star signatures in the rest-frame near-infrared spectra of three young (~1Gyr), massive (~10^10Msun) quiescent galaxies at large look-back time, z=1-2, using JWST/NIRSpec. The coexistence of oxygen- and carbon-type absorption features, spectral edges and features from rare species, such as vanadium and possibly zirconium, reveal a strong contribution from TP-AGB stars. Population synthesis models with a significant TP-AGB contribution reproduce the observations better than those with a weak TP-AGB, which are commonly used. These findings call for revisions of published stellar population fitting results, as they point to populations with lower masses and younger ages and have further implications for cosmic dust production and chemical enrichment. New generations of improved models are needed, informed by these and future observations.
MKIDGen3: Energy-Resolving, Single-Photon-Counting MKID Readout on an RFSoC
14 Jun 2024
Building large, cryogenic MKID arrays requires processing highly-multiplexed, wideband readout signals in real time; a task that has previously required large, heavy, and power-intensive custom electronics. In this work, we present the third-generation UVOIR MKID readout system (Gen3) which is capable of reading out twice as many detectors with a fifth the weight and power and an order of magnitude less volume and cost-per-pixel as compared to the previous system. Gen3 leverages the Xilinx RFSoC4x2 platform to read out 2048, 1 MHz MKID channels per board. The system takes a modern approach to FPGA design using Vitis High-Level Synthesis (HLS) to specify signal processing blocks in C/C++, Vivado ML Intelligent Design Runs (IDR) to inform implementation stragety and close timing, and Python Productivity for ZYNQ (PYNQ) to simplify interacting with and programming the FPGA using Python. This design suite and tool flow allows general users to contribute to and maintain the design and positions Gen3 to rapidly migrate to future platforms as they become available. In this work, we describe the system requirements, design, and implementation. We also provide performance characterization details and show that the system achieves detector-limited resolving power in the case of few readout tones and minimal degradation with all 2048 tones. Planned upgrades and future work are also discussed. The Gen3 MKID readout system is fully open-source and is expected to facilitate future array scaling to megapixel-sized formats and increase the feasibility of deploying UVOIR MKIDs in space.
Large Language Model Agent: A Survey on Methodology, Applications and Challenges
27 Mar 2025
University of Washington
Harvard UniversityA comprehensive survey from an international research consortium led by Peking University examines Large Language Model (LLM) agents through a methodology-centered taxonomy, analyzing their construction, collaboration mechanisms, and evolution while providing a unified architectural framework for understanding agent systems across different application domains.
View blog
Foundational Models Defining a New Era in Vision: A Survey and Outlook
25 Jul 2023
rao anwer
Google ResearchA comprehensive survey of vision foundational models categorizes them based on prompting mechanisms into textually prompted, visually prompted, heterogeneous modality-based, and embodied models. It details their architectures, training objectives, and diverse applications, highlighting capabilities like zero-shot learning and multimodal understanding.
View blog
Advanced Long-term Earth System Forecasting
National University of Singapore
University of Science and Technology of ChinaTritonCast, a hierarchical deep learning framework, tackles the spectral bias problem in AI models to achieve unprecedented long-term stability and physical fidelity in Earth system forecasting. The model delivers stable year-long global weather predictions and multi-year climate simulations, while also demonstrating state-of-the-art accuracy in medium-range weather, high-fidelity ocean dynamics, and robust zero-shot generalization across spatial resolutions.
View blog
MMReason: An Open-Ended Multi-Modal Multi-Step Reasoning Benchmark for MLLMs Toward AGI
30 Jun 2025
Tsinghua UniversityReasoning plays a crucial role in advancing Multimodal Large Language Models (MLLMs) toward Artificial General Intelligence. However, existing MLLM benchmarks often fall short in precisely and comprehensively evaluating long-chain reasoning abilities from three key aspects: (1) lack of difficulty and diversity, (2) susceptibility to guessability and memorization, (3) inadequate assessment of intermediate reasoning steps. To fill this gap, we introduce MMReason, a new benchmark designed to precisely and comprehensively evaluate MLLM long-chain reasoning capability with diverse, open-ended, challenging questions. First, we curate challenging questions requiring multi-step reasoning from various fields (i.e., 6 disciplines) and multiple difficulty levels (i.e., from pre-university to university, and from foundational to competition tiers). Second, these questions are reformulated into an open-ended format and filtered using a multi-model voting technique to eliminate shortcut cases related to guessing and memorization, ensuring robust reasoning evaluations. Third, we annotate the questions with detailed step-by-step solutions, and design a reference-based ternary scoring mechanism to reliably assess intermediate reasoning steps. With MMReason, we benchmark popular leading MLLMs and provide an in-depth analysis of their reasoning capabilities. We hope MMReason will serve as a valuable resource for advancing MLLM reasoning research. Code will be available at this https URL.
Recent Advances in 3D Gaussian Splatting
Chinese Academy of SciencesThis paper provides a comprehensive review of 3D Gaussian Splatting (3DGS), an explicit 3D representation that achieves photorealistic novel view synthesis with real-time rendering and significantly faster training times than previous methods like NeRF, enabling new applications in 3D content creation and interaction. The review consolidates advancements across 3D reconstruction, editing, and generative tasks.
View blog
Omni-Mol: Multitask Molecular Model for Any-to-any Modalities
University of MarylandIn the molecular domain, numerous studies have explored the use of multimodal large language models (LLMs) to construct a general-purpose, multi-task molecular model. However, these efforts are still far from achieving a truly universal molecular model. We identify three key challenges in this endeavor: (1) Existing molecular task datasets are typically small in scale and lack comprehensive domain coverage. (2) Tasks from different molecular subfields are difficult to effectively learn jointly through LLMs due to significant distributional shifts and competition among tasks, which introduces instability in the learning process. (3) Both inter-task and intra-task molecular representations demand different intrinsic dimensions in the language space, making it challenging to balance between redundancy and insufficiency in language model representations. To address these challenges, we innovatively categorize existing small-molecule tasks into four types: Mol2Mol, Mol2Text, Mol2Num, and Text2Mol. We then collect a dataset encompassing over 16 tasks with more than 1.4 million samples, making it the largest molecular instruction-tuning dataset to date. Leveraging the extensive pretraining of LLMs on existing chemical literature, we propose a novel multimodal LLM framework, named Omni-Mol, which unifies all small-molecule tasks and supports both molecular generation and understanding. The core of Omni-Mol is our proposed MoGE, which dynamically adapts to the intrinsic rank of different tasks. This mixture-of-experts architecture enhances the model's ability to handle diverse tasks and modalities effectively. Our model achieves unified instruction tuning across 16 tasks and attains state-of-the-art performance on 13 of them. Extensive experiments further demonstrate the scalability and versatility of Omni-Mol.
Feature Learning beyond the Lazy-Rich Dichotomy: Insights from Representational Geometry
11 Jul 2025
New York University
Flatiron InstituteResearchers at the Flatiron Institute introduced a representation-based framework using "manifold capacity" and "Geometry Linked to Untangling Efficiency" (GLUE) measures to characterize feature learning in neural networks. This approach offers a detailed understanding beyond the binary lazy-rich dichotomy, revealing distinct learning stages and strategies, and providing geometric explanations for out-of-distribution generalization failures.
View blog
Unified and consistent structure growth measurements from joint ACT, SPT and \textit{Planck} CMB lensing
28 Apr 2025
We present the tightest cosmic microwave background (CMB) lensing constraints
to date on the growth of structure by combining CMB lensing measurements from
the Atacama Cosmology Telescope (ACT), the South Pole Telescope (SPT) and
\textit{Planck}. Each of these surveys individually provides lensing
measurements with similarly high statistical power, achieving signal-to-noise
ratios of approximately 40. The combined lensing bandpowers represent the most
precise CMB lensing power spectrum measurement to date with a signal-to-noise
ratio of 61 and an amplitude of $A_\mathrm{lens}^\mathrm{recon} = 1.025 \pm
0.017$ with respect to the theory prediction from the best-fit CMB
\textit{Planck}-ACT cosmology. The bandpowers from all three lensing datasets,
analyzed jointly, yield a measurement of the parameter combination
$S_8^\mathrm{CMBL} \equiv \sigma_8\,(\Omega_m/0.3)^{0.25} =
0.825^{+0.015}_{-0.013}$. Including Dark Energy Spectroscopic Instrument (DESI)
Baryon Acoustic Oscillation (BAO) data improves the constraint on the amplitude
of matter fluctuations to (a
determination). When combining with uncalibrated supernovae from
\texttt{Pantheon+}, we present a sound-horizon-independent estimate of
. The joint lensing constraints
on structure growth and present-day Hubble rate are fully consistent with a
CDM model fit to the primary CMB data from \textit{Planck} and ACT.
While the precise upper limit is sensitive to the choice of data and underlying
model assumptions, when varying the neutrino mass sum within the
cosmological model, the combination of primary CMB, BAO
and CMB lensing drives the probable upper limit for the mass sum towards lower
values, comparable to the minimum mass prior required by neutrino oscillation
experiments.
Multi-modal Sensor Fusion for Auto Driving Perception: A Survey
16 Dec 2024
Researchers from Shanghai AI Lab and UCLA propose a new, refined taxonomy for multi-modal sensor fusion methods in autonomous driving perception, classifying them into strong-fusion (early, deep, late, asymmetry) and weak-fusion categories. The work also identifies and categorizes existing challenges and outlines future research directions to improve perception system robustness.
View blog
Quantum Linear System Solvers: A Survey of Algorithms and Applications
University of TorontoA comprehensive survey categorizes and analyzes quantum algorithms for solving linear systems, detailing advancements since HHL and critically assessing their complexities and practical applications. The paper presents a novel taxonomy of QLSP solvers and revisits key limitations, highlighting algorithms that achieve optimal scaling while emphasizing persistent challenges in state preparation and output.
View blog
On the quantum mechanics of entropic forces
Lawrence Berkeley National LaboratoryCarney et al. construct quantum mechanical models where Newton's law of gravitation emerges from the thermodynamic properties of microscopic quantum systems, providing a framework to test if gravity is an emergent phenomenon. The models predict distinct experimental signatures, including anomalous force noise, enhanced spatial decoherence, and altered entanglement generation compared to standard quantum gravity.
View blog
Cosmology From CMB Lensing and Delensed EE Power Spectra Using 2019-2020 SPT-3G Polarization Data
Michigan State University
University of Illinois at Urbana-ChampaignFrom CMB polarization data alone we reconstruct the CMB lensing power spectrum, comparable in overall constraining power to previous temperature-based reconstructions, and an unlensed E-mode power spectrum. The observations, taken in 2019 and 2020 with the South Pole Telescope (SPT) and the SPT-3G camera, cover 1500 deg at 95, 150, and 220 GHz with arcminute resolution and roughly 4.9K-arcmin coadded noise in polarization. The power spectrum estimates, together with systematic parameter estimates and a joint covariance matrix, follow from a Bayesian analysis using the Marginal Unbiased Score Expansion (MUSE) method. The E-mode spectrum at and lensing spectrum at are the most precise to date. Assuming the CDM model, and using only these SPT data and priors on and absolute calibration from Planck, we find km/s/Mpc, comparable in precision to the Planck determination and in 5.4 tension with the most precise inference derived via the distance ladder. We also find , providing further independent evidence of a slight tension with low-redshift structure probes. The CDM model provides a good simultaneous fit to the combined Planck, ACT, and SPT data, and thus passes a powerful test. Combining these CMB datasets with BAO observations, we find that the effective number of neutrino species, spatial curvature, and primordial helium fraction are consistent with standard model values, and that the 95% confidence upper limit on the neutrino mass sum is 0.075 eV. The SPT data are consistent with the somewhat weak preference for excess lensing power seen in Planck and ACT data relative to predictions of the CDM model. We also detect at greater than 3 the influence of non-linear evolution in the CMB lensing power spectrum and discuss it in the context of the tension.(abridged)
IMAGHarmony: Controllable Image Editing with Consistent Object Quantity and Layout
Recent diffusion models have advanced image editing by improving fidelity and controllability across creative and personalized applications. However, multi-object scenes remain challenging, as reliable control over object categories, counts, and spatial layout is difficult to achieve. For that, we first study quantity and layout consistent image editing, abbreviated as QL-Edit, which targets control of object quantity and spatial layout in multi-object scenes. Then, we present IMAGHarmony, a straightforward framework featuring a plug-and-play harmony aware (HA) module that fuses perception semantics while modeling object counts and locations, resulting in accurate edits and strong structural consistency. We further observe that diffusion models are sensitive to the choice of initial noise and tend to prefer certain noise patterns. Based on this finding, we present a preference-guided noise selection (PNS) strategy that selects semantically aligned initial noise through vision and language matching, thereby further improving generation stability and layout consistency in multiple object editing. To support evaluation, we develop HarmonyBench, a comprehensive benchmark that covers a diverse range of quantity and layout control scenarios. Extensive experiments demonstrate that IMAGHarmony outperforms prior methods in both structural alignment and semantic accuracy, utilizing only 200 training images and 10.6M of trainable parameters. Code, models, and data are available at this https URL.
CAPERS-LRD-z9: A Gas Enshrouded Little Red Dot Hosting a Broad-line AGN at z=9.288
Beijing Normal UniversityWe present CAPERS-LRD-z9, a little red dot (LRD) which we confirm to be a broad-line AGN (BLAGN). First identified as a high-redshift LRD candidate from PRIMER NIRCam photometry, follow-up NIRSpec/PRISM spectroscopy of CAPERS-LRD-z9 from the CANDELS-Area Prism Epoch of Reionization Survey (CAPERS) has revealed a broad km s H emission line and narrow [O III] lines, indicative of a BLAGN. Based on the broad H line, we compute a canonical black-hole mass of , although full consideration of systematic uncertainties yields a conservative range of 6.65<\log(M_{\textrm{BH}}/M_{\odot})<8.50. These observations suggest that either a massive black hole seed, or a lighter stellar remnant seed undergoing periods of super-Eddington accretion, is necessary to grow such a massive black hole in Myr of cosmic time. CAPERS-LRD-z9 exhibits a strong Balmer break, consistent with a central AGN surrounded by dense () neutral gas. We model CAPERS-LRD-z9 using CLOUDY to fit the emission red-ward of the Balmer break with a dense gas-enshrouded AGN, and bagpipes to fit the rest-ultraviolet emission as a host-galaxy stellar population. This upper limit on the stellar mass of the host galaxy (<10^9\,{\rm M_\odot}) implies that the black-hole to stellar mass ratio may be extremely large, possibly >5\% (although systematic uncertainties on the black-hole mass prevent strong conclusions). However, the shape of the UV continuum differs from typical high-redshift star-forming galaxies, indicating that this UV emission may also be of AGN origin, and hence the true stellar mass of the host may be still lower.
Rango: Adaptive Retrieval-Augmented Proving for Automated Software Verification
28 Jan 2025
Kyle Thompson
Imperial College LondonFormal verification using proof assistants, such as Coq, enables the creation
of high-quality software. However, the verification process requires
significant expertise and manual effort to write proofs. Recent work has
explored automating proof synthesis using machine learning and large language
models (LLMs). This work has shown that identifying relevant premises, such as
lemmas and definitions, can aid synthesis. We present Rango, a fully automated
proof synthesis tool for Coq that automatically identifies relevant premises
and also similar proofs from the current project and uses them during
synthesis. Rango uses retrieval augmentation at every step of the proof to
automatically determine which proofs and premises to include in the context of
its fine-tuned LLM. In this way, Rango adapts to the project and to the
evolving state of the proof. We create a new dataset, CoqStoq, of 2,226
open-source Coq projects and 196,929 theorems from GitHub, which includes both
training data and a curated evaluation benchmark of well-maintained projects.
On this benchmark, Rango synthesizes proofs for 32.0% of the theorems, which is
29% more theorems than the prior state-of-the-art tool Tactician. Our
evaluation also shows that Rango adding relevant proofs to its context leads to
a 47% increase in the number of theorems proven.
Introduction to Cosmic F- and D-Strings
21 Dec 2004
In these lectures I discuss the possibility that superstrings of cosmic
length might exist and be observable. I first review the original idea of
cosmic strings arising as gauge theory solitons, and discuss in particular
their network properties and the observational bounds that rule out cosmic
strings as the principal origin of structure in our universe. I then consider
cosmic superstrings, including the `fundamental' F-strings and also D-strings
and strings arising from wrapped branes. I discuss the conditions under which
these will exist and be observable, and ways in which different kinds of string
might be distinguished. We will see that each of these issues is
model-dependent, but that some of the simplest models of inflation in string
theory do lead to cosmic superstrings. Moreover, these could be the first
objects seen in gravitational wave astronomy, and might have distinctive
network properties. The outline of these lectures follows hep-th/0410082, but
the treatment is more detailed and pedagogical.
pathfinder: A Semantic Framework for Literature Review and Knowledge
Discovery in Astronomy
University of ManchesterThe pathfinder framework provides a semantic search and synthesis tool for astronomical literature, complementing existing databases by addressing information overload. It processes natural language queries to identify relevant papers and synthesize information, achieving improved retrieval accuracy and user satisfaction on various benchmarks.
View blog
Dynamical Dark Energy in light of the DESI DR2 Baryonic Acoustic Oscillations Measurements
Understanding whether cosmic acceleration arises from a cosmological constant or a dynamical component is a central goal of cosmology, and the Dark Energy Spectroscopic Instrument (DESI) enables stringent tests with high-precision distance measurements. We analyze baryon acoustic oscillation (BAO) measurements from DESI Data Release 1 (DR1) and Data Release 2 (DR2), combined with Type Ia supernovae and a cosmic microwave background (CMB) distance prior. With the larger statistical power and wider redshift coverage of DR2, the preference for dynamical dark energy does not diminish relative to DR1. Using both a shape-function reconstruction and non-parametric approaches with a Horndeski-motivated correlation prior, we find that the dark-energy equation of state varies with redshift. BAO data alone yield modest constraints, but in combination with independent supernova compilations and the CMB prior they strengthen the evidence for dynamics. Bayesian model comparison shows moderate support for departures from CDM when multiple degrees of freedom in are allowed, corresponding to tension with CDM (and higher for some data sets). Despite methodological differences, our results are consistent with companion DESI papers, underscoring the complementarity of approaches. Possible systematics remain under study; forthcoming DESI, \emph{Euclid}, and next-generation CMB data will provide decisive tests.
There are no more papers matching your filters at the moment.