We present an overview of the JWST GLIMPSE program, highlighting its survey design, primary science goals, gravitational lensing models, and first results. GLIMPSE provides ultra-deep JWST/NIRCam imaging across seven broadband filters (F090W, F115W, F200W, F277W, F356W, F444W) and two medium-band filters (F410M, F480M), with exposure times ranging from 20 to 40 hours per filter. This yields a 5$σ$ limiting magnitude of 30.9 AB (measured in a 0.2 arcsec diameter aperture). The field is supported by extensive ancillary data, including deep HST imaging from the Hubble Frontier Fields program, VLT/MUSE spectroscopy, and deep JWST/NIRSpec medium-resolution multi-object spectroscopy. Exploiting the strong gravitational lensing of the galaxy cluster Abell S1063, GLIMPSE probes intrinsic depths beyond 33 AB magnitudes and covers an effective source-plane area of approximately 4.4 arcmin$^2$ at $z \sim 6$. The program's central aim is to constrain the abundance of the faintest galaxies from $z \sim 6$ up to the highest redshifts, providing crucial benchmarks for galaxy formation models, which have so far been tested primarily on relatively bright systems. We present an initial sample of $\sim 540$ galaxy candidates identified at $6 < z < 16$, with intrinsic UV magnitudes spanning $M_{\mathrm UV}$ = $-$20 to $-$12. This enables unprecedented constraints on the extreme faint end of the UV luminosity function at these epochs. In addition, GLIMPSE opens new windows for spatially resolved studies of star clusters in early galaxies and the detection and characterization of faint high-$z$ active galactic nuclei. This paper accompanies the first public data release, which includes reduced JWST and HST mosaics, photometric catalogs, and gravitational lensing models.

Fast and flexible 3D scene reconstruction from unstructured image collections remains a significant challenge. We present YoNoSplat, a feedforward model that reconstructs high-quality 3D Gaussian Splatting representations from an arbitrary number of images. Our model is highly versatile, operating effectively with both posed and unposed, calibrated and uncalibrated inputs. YoNoSplat predicts local Gaussians and camera poses for each view, which are aggregated into a global representation using either predicted or provided poses. To overcome the inherent difficulty of jointly learning 3D Gaussians and camera parameters, we introduce a novel mixing training strategy. This approach mitigates the entanglement between the two tasks by initially using ground-truth poses to aggregate local Gaussians and gradually transitioning to a mix of predicted and ground-truth poses, which prevents both training instability and exposure bias. We further resolve the scale ambiguity problem by a novel pairwise camera-distance normalization scheme and by embedding camera intrinsics into the network. Moreover, YoNoSplat also predicts intrinsic parameters, making it feasible for uncalibrated inputs. YoNoSplat demonstrates exceptional efficiency, reconstructing a scene from 100 views (at 280x518 resolution) in just 2.69 seconds on an NVIDIA GH200 GPU. It achieves state-of-the-art performance on standard benchmarks in both pose-free and pose-dependent settings. Our project page is at https://botaoye.github.io/yonosplat/.

Dynamic Gaussian Splatting approaches have achieved remarkable performance for 4D scene reconstruction. However, these approaches rely on dense-frame video sequences for photorealistic reconstruction. In real-world scenarios, due to equipment constraints, sometimes only sparse frames are accessible. In this paper, we propose Sparse4DGS, the first method for sparse-frame dynamic scene reconstruction. We observe that dynamic reconstruction methods fail in both canonical and deformed spaces under sparse-frame settings, especially in areas with high texture richness. Sparse4DGS tackles this challenge by focusing on texture-rich areas. For the deformation network, we propose Texture-Aware Deformation Regularization, which introduces a texture-based depth alignment loss to regulate Gaussian deformation. For the canonical Gaussian field, we introduce Texture-Aware Canonical Optimization, which incorporates texture-based noise into the gradient descent process of canonical Gaussians. Extensive experiments show that when taking sparse frames as inputs, our method outperforms existing dynamic or few-shot techniques on NeRF-Synthetic, HyperNeRF, NeRF-DS, and our iPhone-4D datasets.

Slack's phonon-glass and electron-crystal concept has been the guiding paradigm for designing new thermoelectric materials. Zintl phases, in principle, have been shown as great contenders of the concept and thereby good thermoelectric candidates. With this as motivation, we design new Zintl phases SrBaX (X = Si, Ge, Sn) using state-of-the-art computational methods. Herein, we use first-principles simulations to provide key theoretical insights to thermal and electrical transport properties. Some of the key findings of our work feature remarkably low lattice thermal conductivities ($<$~1~W~m$^{-1}$~K$^{-1}$), putting proposed materials among the well-known thermoelectric materials such as SnSe and other contemporary Zintl phases. We ascribe such low values to antibonding states induced weak bonding in the lattice and intrinsically weak phonon transport, resulting in low phonon velocities, short lifetimes, and considerable anharmonic scattering phase spaces. Besides, our results on electronic structure and transport properties reveal tremendous performance of SrBaGe ($ZT\sim$ 2.0 at 700~K), highlighting the relevance among state-of-the-art materials such as SnSe. Further, the similar performances for both $p$- and $n$-type dopings render these materials attractive from device fabrication perspective. We believe that our study would invite experimental investigations for realizing the true thermoelectric potential of SrBaX series.

Accurate 3D reconstruction from multi-view images is essential for downstream robotic tasks such as navigation, manipulation, and environment understanding. However, obtaining precise camera poses in real-world settings remains challenging, even when calibration parameters are known. This limits the practicality of existing NeRF-based methods that rely heavily on accurate extrinsic estimates. Furthermore, their implicit volumetric representations differ significantly from the widely adopted polygonal meshes, making rendering and manipulation inefficient in standard 3D software. In this work, we propose a robust framework that reconstructs high-quality, editable 3D meshes directly from multi-view images with noisy extrinsic parameters. Our approach jointly refines camera poses while learning an implicit scene representation that captures fine geometric detail and photorealistic appearance. The resulting meshes are compatible with common 3D graphics and robotics tools, enabling efficient downstream use. Experiments on standard benchmarks demonstrate that our method achieves accurate and robust 3D reconstruction under pose uncertainty, bridging the gap between neural implicit representations and practical robotic applications.

The ability to control the magnetic state provides a powerful means to tune the underlying band topology, enabling transitions between distinct electronic phases and the emergence of novel quantum phenomena. In this work, we address the evolution of ferromagnetic state upon applying external pressures up to 10.8~GPa using a combined experimental and theoretical study. The standard \emph{ab initio} Density Functional Theory computation including ionic relaxations grossly overestimates the unit cell magnetization as a function of pressure. In our theoretical analysis we identify two possible mechanisms to remedy this shortcoming. Matching the experimental observations is achieved by a symmetry-preserving adjustment of the sulfur atoms position within the unit cell. Alternatively, we explore various combinations of the exchange and correlation parts of the effective potential which reproduce the experimental magnetization, the structural parameters and the measured optical conductivity spectra. Thus, the pressure-dependent behavior of magnetization demands a careful theoretical treatment and analysis of theoretical and experimental data.

Friction accounts for up to 30% of global energy consumption, underscoring the urgent need for superlubricity in advanced materials. Two-dimensional (2D) electrides are layered materials with cationic layers separated by 2D confined electrons that act as anions. This study reveals the unique frictional properties of these compounds and the underlying mechanisms. We establish that interlayer friction correlates with the cationic charges and sliding-induced charge redistribution. Remarkably, the 2D electride Ba2N stands out for its lower interlayer friction than graphene, despite its stronger interlayer adhesion, defying conventional tribological understanding. This anomalous behavior arises from electron redistribution as the dominant energy dissipation pathway. Combining ab initio calculations and deep potential molecular dynamics (DPMD) simulations, we show that incommensurate twisted interfaces (2° < θ < 58°) in Ba2N achieve structural superlubricity by suppressing out-of-plane buckling and energy corrugation. Notably, a critical normal load of 2.3 GPa enables barrier-free sliding in commensurate Ba2N (θ = 0°), with an ultralow shear-to-load ratio of 0.001, suggesting the potential for superlubricity. Moreover, electron doping effectively reduces interlayer friction by controllably modulating stacking energies in 2D electrides. These findings establish 2D electrides as a transformative platform for energy-efficient tribology, enabling scalable superlubricity through twist engineering, load adaptation, or electrostatic gating. Our work advances the fundamental understanding of electron-mediated friction, with Ba2N serving a model system for cost-effective, high-performance material design.

With the rapid advancement of 3D visualization, 3D Gaussian Splatting (3DGS) has emerged as a leading technique for real-time, high-fidelity rendering. While prior research has emphasized algorithmic performance and visual fidelity, the perceptual quality of 3DGS-rendered content, especially under varying reconstruction conditions, remains largely underexplored. In practice, factors such as viewpoint sparsity, limited training iterations, point downsampling, noise, and color distortions can significantly degrade visual quality, yet their perceptual impact has not been systematically studied. To bridge this gap, we present 3DGS-QA, the first subjective quality assessment dataset for 3DGS. It comprises 225 degraded reconstructions across 15 object types, enabling a controlled investigation of common distortion factors. Based on this dataset, we introduce a no-reference quality prediction model that directly operates on native 3D Gaussian primitives, without requiring rendered images or ground-truth references. Our model extracts spatial and photometric cues from the Gaussian representation to estimate perceived quality in a structure-aware manner. We further benchmark existing quality assessment methods, spanning both traditional and learning-based approaches. Experimental results show that our method consistently achieves superior performance, highlighting its robustness and effectiveness for 3DGS content evaluation. The dataset and code are made publicly available at https://github.com/diaoyn/3DGSQA to facilitate future research in 3DGS quality assessment.

Talking Face Generation (TFG) aims to produce realistic and dynamic talking portraits, with broad applications in fields such as digital education, film and television production, e-commerce live streaming, and other related areas. Currently, TFG methods based on Neural Radiated Field (NeRF) or 3D Gaussian sputtering (3DGS) are received widespread attention. They learn and store personalized features from reference videos of each target individual to generate realistic speaking videos. To ensure models can capture sufficient 3D information and successfully learns the lip-audio mapping, previous studies usually require meticulous processing and fitting several minutes of reference video, which always takes hours. The computational burden of processing and fitting long reference videos severely limits the practical application value of these methods.However, is it really necessary to fit such minutes of reference video? Our exploratory case studies show that using some informative reference video segments of just a few seconds can achieve performance comparable to or even better than the full reference video. This indicates that video informative quality is much more important than its length. Inspired by this observation, we propose the ISExplore (short for Informative Segment Explore), a simple-yet-effective segment selection strategy that automatically identifies the informative 5-second reference video segment based on three key data quality dimensions: audio feature diversity, lip movement amplitude, and number of camera views. Extensive experiments demonstrate that our approach increases data processing and training speed by more than 5x for NeRF and 3DGS methods, while maintaining high-fidelity output. Project resources are available at xx.

Whether the time-dependent Aharonov-Bohm (AB) effect even exists or not has been the subject of long-standing debate. There are two factors complicating the problem. First, in the closed spacetime line integral of the vector potential that is thought to give the AB-phase shift, how to treat the time-varying vector potential is highly nontrivial. Second, the time-varying magnetic flux generates induced electric field even outside the solenoid. In the present paper, motivated by a recent work by Gao, we re-investigate the role of the induced electric field with the utmost care. This analysis reveals a highly nontrivial effect of the induced electric field, which turns out to be useful for verifying the very existence of the time-dependent AB-effect.