arXiv subjects

Cheng Peng

Publications and source records attributed to Cheng Peng.

At least 127 records · Page 7Linked to original sources

arXiv2024

Correlated Electronic Structure and Incipient Flat Bands of the Kagome Superconductor CsCr3Sb5

Kagome materials exhibit many novel phenomena emerging from the interplay between lattice geometry, electronic structure, and topology. A prime example is the vanadium-based kagome materials AV3Sb5 (A = K, Rb, and Cs) with superconductivity and unconventional charge-density wave (CDW). More interestingly, the substitution of vanadium by chromium further introduces magnetism and enhances the correlation effect in CsCr3Sb5 which likewise exhibits superconductivity under pressure and competing density-wave state. Here we systematically investigate the electronic structure of CsCr3Sb5 using high-resolution angle-resolved photoemission spectroscopy (APRES) and ab-initio calculations. Overall, the measured electronic structure agrees with the theoretical calculation. Remarkably, Cr 3d orbitals exhibit incoherent electronic states and contribute to incipient flat bands close to the Fermi level. The electronic structure shows a minor change across the magnetic transition at 55 K, suggesting a weak interplay between the local magnetic moment and itinerant electrons. Furthermore, we reveal a drastic enhancement of the electron scattering rate across the magnetic transition, which is relevant to the semiconducting-like transport property of the system at high temperatures. Our results suggest that CsCr3Sb5 is a strongly correlated Hund's metal with incipient flat bands near the Fermi level, which provides an electronic basis for understanding its novel properties in comparison to the non-magnetic and weakly correlated AV3Sb5.

cond-mat.str-el↗

arXiv2024

LP-3DGS: Learning to Prune 3D Gaussian Splatting

Recently, 3D Gaussian Splatting (3DGS) has become one of the mainstream methodologies for novel view synthesis (NVS) due to its high quality and fast rendering speed. However, as a point-based scene representation, 3DGS potentially generates a large number of Gaussians to fit the scene, leading to high memory usage. Improvements that have been proposed require either an empirical and preset pruning ratio or importance score threshold to prune the point cloud. Such hyperparamter requires multiple rounds of training to optimize and achieve the maximum pruning ratio, while maintaining the rendering quality for each scene. In this work, we propose learning-to-prune 3DGS (LP-3DGS), where a trainable binary mask is applied to the importance score that can find optimal pruning ratio automatically. Instead of using the traditional straight-through estimator (STE) method to approximate the binary mask gradient, we redesign the masking function to leverage the Gumbel-Sigmoid method, making it differentiable and compatible with the existing training process of 3DGS. Extensive experiments have shown that LP-3DGS consistently produces a good balance that is both efficient and high quality.

cs.CV↗

arXiv2024

Accurate and efficient protein embedding using multi-teacher distillation learning

Motivation: Protein embedding, which represents proteins as numerical vectors, is a crucial step in various learning-based protein annotation/classification problems, including gene ontology prediction, protein-protein interaction prediction, and protein structure prediction. However, existing protein embedding methods are often computationally expensive due to their large number of parameters, which can reach millions or even billions. The growing availability of large-scale protein datasets and the need for efficient analysis tools have created a pressing demand for efficient protein embedding methods. Results: We propose a novel protein embedding approach based on multi-teacher distillation learning, which leverages the knowledge of multiple pre-trained protein embedding models to learn a compact and informative representation of proteins. Our method achieves comparable performance to state-of-the-art methods while significantly reducing computational costs and resource requirements. Specifically, our approach reduces computational time by ~70\% and maintains almost the same accuracy as the original large models. This makes our method well-suited for large-scale protein analysis and enables the bioinformatics community to perform protein embedding tasks more efficiently.

q-bio.GN↗

arXiv2024

Using magnetic dynamics to measure the spin gap in a candidate Kitaev material

Materials potentially hosting Kitaev spin-liquid states are considered crucial for realizing topological quantum computing. However, the intricate nature of spin interactions within these materials complicates the precise measurement of low-energy spin excitations indicative of fractionalized excitations. Using Na$_{2}$Co$_2$TeO$_{6}$ as an example, we study these low-energy spin excitations using the time-resolved resonant elastic x-ray scattering (tr-REXS). Our observations unveil remarkably slow spin dynamics at the magnetic peak, whose recovery timescale is several nanoseconds. This timescale aligns with the extrapolated spin gap of $\sim$ 1 $μ$eV, obtained by density matrix renormalization group (DMRG) simulations in the thermodynamic limit. The consistency demonstrates the efficacy of tr-REXS in discerning low-energy spin gaps inaccessible to conventional spectroscopic techniques.

cond-mat.str-el↗

arXiv2024

The emergence of antiferromagnetic correlations and Kondo-like features in a two-band model for infinite-layer nickelates

We report a determinant quantum Monte Carlo study of a two-band model, inspired by infinite-layer nickelates, focusing on the influence of interlayer hybridization between $3d_{x^2-y^2}$ orbitals derived from Ni (or Ni and O) in one layer and rare-earth ($R$) 5d orbitals in the other layer, hereafter the NI and $R$ layers, respectively. For a filling with one electron shared between the two layers on average, interlayer hybridization leads to "self-doped" holes in the Ni layer and the absence of antiferromagnetic ordering, but rather the appearance of spin-density and charge-density stripe-like states. As the interlayer hybridization increases, both the Ni and $R$ layers develop antiferromagnetic correlations, even though either layer individually remains away from half-filling. For hybridization within an intermediate range, roughly comparable to the intralayer nearest-neighbor hopping $t_{\text{Ni}}$, the model develops signatures of Kondo-like physics.

cond-mat.str-el↗

arXiv2024

BAGS: Blur Agnostic Gaussian Splatting through Multi-Scale Kernel Modeling

Recent efforts in using 3D Gaussians for scene reconstruction and novel view synthesis can achieve impressive results on curated benchmarks; however, images captured in real life are often blurry. In this work, we analyze the robustness of Gaussian-Splatting-based methods against various image blur, such as motion blur, defocus blur, downscaling blur, \etc. Under these degradations, Gaussian-Splatting-based methods tend to overfit and produce worse results than Neural-Radiance-Field-based methods. To address this issue, we propose Blur Agnostic Gaussian Splatting (BAGS). BAGS introduces additional 2D modeling capacities such that a 3D-consistent and high quality scene can be reconstructed despite image-wise blur. Specifically, we model blur by estimating per-pixel convolution kernels from a Blur Proposal Network (BPN). BPN is designed to consider spatial, color, and depth variations of the scene to maximize modeling capacity. Additionally, BPN also proposes a quality-assessing mask, which indicates regions where blur occur. Finally, we introduce a coarse-to-fine kernel optimization scheme; this optimization scheme is fast and avoids sub-optimal solutions due to a sparse point cloud initialization, which often occurs when we apply Structure-from-Motion on blurry images. We demonstrate that BAGS achieves photorealistic renderings under various challenging blur conditions and imaging geometry, while significantly improving upon existing approaches.

cs.CV↗

arXiv2024

Finite final segments of the d.c.e. Turing degrees

We prove that every finite distributive lattice is isomorphic to a final segment of the d.c.e. Turing degrees (i.e., the degrees of differences of computably enumerable sets). As a corollary, we are able to infer the undecidability of the EAE-theory of the d.c.e. degrees in the language of partial ordering.

math.LO↗

arXiv2024

Improving Generalizability of Extracting Social Determinants of Health Using Large Language Models through Prompt-tuning

The progress in natural language processing (NLP) using large language models (LLMs) has greatly improved patient information extraction from clinical narratives. However, most methods based on the fine-tuning strategy have limited transfer learning ability for cross-domain applications. This study proposed a novel approach that employs a soft prompt-based learning architecture, which introduces trainable prompts to guide LLMs toward desired outputs. We examined two types of LLM architectures, including encoder-only GatorTron and decoder-only GatorTronGPT, and evaluated their performance for the extraction of social determinants of health (SDoH) using a cross-institution dataset from the 2022 n2c2 challenge and a cross-disease dataset from the University of Florida (UF) Health. The results show that decoder-only LLMs with prompt tuning achieved better performance in cross-domain applications. GatorTronGPT achieved the best F1 scores for both datasets, outperforming traditional fine-tuned GatorTron by 8.9% and 21.8% in a cross-institution setting, and 5.5% and 14.5% in a cross-disease setting.

cs.CL↗

arXiv2024

Kitaev physics in the two-dimensional magnet NiPSe$_3$

The Kitaev interaction, found in candidate materials such as $α$-RuCl$_3$, occurs through the metal ($M$)-ligand ($X$)-metal ($M$) paths of the edge-sharing octahedra because the large spin-orbit coupling (SOC) on the metal atoms activates directional spin interactions. Here, we show that even in $3d$ transition-metal compounds, where the SOC of the metal atom is negligible, heavy ligands can induce bond-dependent Kitaev interactions. In this work, we take as an example the $3d$ transition-metal chalcogenophosphate NiPSe$_3$ and show that the key is found in the presence of a sizable SOC on the Se $p$ orbital, one which mediates the super-exchange between the nearest-neighbor Ni sites. Our study provides a pathway for engineering enhanced Kitaev interactions through the interplay of SOC strength, lattice distortions, and chemical substitutions.

cond-mat.str-el↗

arXiv2024

Particle-hole asymmetric ferromagnetism and spin textures in the triangular Hubbard-Hofstadter model

In a lattice model subject to a perpendicular magnetic field, when the lattice constant is comparable to the magnetic length, one enters the "Hofstadter regime," where continuum Landau levels become fractal magnetic Bloch bands. Strong mixing between bands alters the nature of the resulting quantum phases compared to the continuum limit; lattice potential, magnetic field, and Coulomb interaction must be treated on equal footing. Using determinant quantum Monte Carlo (DQMC) and density matrix renormalization group (DMRG) techniques, we study this regime numerically in the context of the Hubbard-Hofstadter model on a triangular lattice. In the field-filling phase diagram, we find a broad wedge-shaped region of ferromagnetic ground states for filling factor $ν\leq 1$, bounded below by filling factor $ν= 1$ and bounded above by half-filling the lowest Hofstadter subband. We observe signatures of SU(2) quantum Hall ferromagnetism at filling factors $ν=1$ and $ν=3$. The phases near $ν=1$ are particle-hole asymmetric, and we observe a rapid decrease in ground state spin polarization consistent with the formation of skyrmions only on the electron doped side. At large fields, above the ferromagnetic wedge, we observe a low-spin metallic region with spin correlations peaked at small momenta. We argue that the phenomenology of this region likely results from exchange interaction mixing fractal Hofstadter subbands. The phase diagram derived beyond the continuum limit points to a rich landscape to explore interaction effects in magnetic Bloch bands.

cond-mat.str-el↗

arXiv2024

Aria Everyday Activities Dataset

We present Aria Everyday Activities (AEA) Dataset, an egocentric multimodal open dataset recorded using Project Aria glasses. AEA contains 143 daily activity sequences recorded by multiple wearers in five geographically diverse indoor locations. Each of the recording contains multimodal sensor data recorded through the Project Aria glasses. In addition, AEA provides machine perception data including high frequency globally aligned 3D trajectories, scene point cloud, per-frame 3D eye gaze vector and time aligned speech transcription. In this paper, we demonstrate a few exemplar research applications enabled by this dataset, including neural scene reconstruction and prompted segmentation. AEA is an open source dataset that can be downloaded from https://www.projectaria.com/datasets/aea/. We are also providing open-source implementations and examples of how to use the dataset in Project Aria Tools https://github.com/facebookresearch/projectaria_tools.

cs.CV↗

arXiv2024

Skyrmions: A review on materials perspective for future electronic devices

Recent years have witnessed an enormous rise in research interest in magnetic skyrmions owing to their capability to improve over contemporary spintronic devices. An overview of the various magnetic interactions responsible for the formation of skyrmion together with distinct noncentrosymmetric and centrosymmetric skyrmion candidates is given in this review article. The magnetic interactions known as Dzyaloshinskii-Moriya interactions (DMI) have been extensively studied over the years to better understand the mechanism of skyrmions in chiral magnets that have larger skyrmion sizes. Because of their low skyrmion size, the centrosymmetric frustrated magnets are dwelling to skyrmions controlled by long-range interactions such as the Ruderman-Kittel-Kasuya-Yosida interaction (RKKY), which may be useful in the development of high-density memory devices. To lay a solid foundation for the magnetic interactions involved in skyrmion formations and many other special physical properties, more research in the field of centrosymmetric skyrmions is required. Apart from studying candidates with low skyrmion sizes, one of the main goals for the future is to better understand the dynamics of skyrmion using polarized magnons, which has the potential to be extremely beneficial for spintronic applications.

cond-mat.mtrl-sci↗

arXiv2024

CARAT: Contrastive Feature Reconstruction and Aggregation for Multi-Modal Multi-Label Emotion Recognition

Multi-modal multi-label emotion recognition (MMER) aims to identify relevant emotions from multiple modalities. The challenge of MMER is how to effectively capture discriminative features for multiple labels from heterogeneous data. Recent studies are mainly devoted to exploring various fusion strategies to integrate multi-modal information into a unified representation for all labels. However, such a learning scheme not only overlooks the specificity of each modality but also fails to capture individual discriminative features for different labels. Moreover, dependencies of labels and modalities cannot be effectively modeled. To address these issues, this paper presents ContrAstive feature Reconstruction and AggregaTion (CARAT) for the MMER task. Specifically, we devise a reconstruction-based fusion mechanism to better model fine-grained modality-to-label dependencies by contrastively learning modal-separated and label-specific features. To further exploit the modality complementarity, we introduce a shuffle-based aggregation strategy to enrich co-occurrence collaboration among labels. Experiments on two benchmark datasets CMU-MOSEI and M3ED demonstrate the effectiveness of CARAT over state-of-the-art methods. Code is available at https://github.com/chengzju/CARAT.

cs.MM↗

arXiv2023

Entanglement of defect subregions in double holography

In the framework of double holography, we investigate the entanglement behavior of a brane subregion in AdS spacetime coupled to a bath on its boundary and also extract the contribution from the quantum matter within this subregion. From the boundary perspective, the brane subregion serves as the entanglement wedge of a subsystem on the conformal defect. In the ground state, we find the subsystem undergoes an entanglement phase transition induced by the degrees of freedom on the brane. With subcritical parameters, the wedge and entanglement entropy sharply decrease to zero. In contrast, in the supercritical regime, both the wedge and entropy stabilize, enabling analysis of both entanglement and reflected entropy. In this phase, we derive formulas for entanglement measures based on defect and bath central charges in the semi-classical limit. For entanglement entropy, the classical geometry only contributes a subleading term with logarithmic divergence, but the brane-bath CFT entanglement exhibits a dominant linear divergence, even in the semi-classical limit. Regarding reflected entropy within the defect subsystem, classical geometry contributes a leading term with logarithmic divergence, while the quantum matter within the entanglement wedge only contributes a finite term.

hep-th↗

arXiv2023

Generative Large Language Models Are All-purpose Text Analytics Engines: Text-to-text Learning Is All Your Need

Objective To solve major clinical natural language processing (NLP) tasks using a unified text-to-text learning architecture based on a generative large language model (LLM) via prompt tuning. Methods We formulated 7 key clinical NLP tasks as text-to-text learning and solved them using one unified generative clinical LLM, GatorTronGPT, developed using GPT-3 architecture and trained with up to 20 billion parameters. We adopted soft prompts (i.e., trainable vectors) with frozen LLM, where the LLM parameters were not updated (i.e., frozen) and only the vectors of soft prompts were updated, known as prompt tuning. We added additional soft prompts as a prefix to the input layer, which were optimized during the prompt tuning. We evaluated the proposed method using 7 clinical NLP tasks and compared them with previous task-specific solutions based on Transformer models. Results and Conclusion The proposed approach achieved state-of-the-art performance for 5 out of 7 major clinical NLP tasks using one unified generative LLM. Our approach outperformed previous task-specific transformer models by ~3% for concept extraction and 7% for relation extraction applied to social determinants of health, 3.4% for clinical concept normalization, 3.4~10% for clinical abbreviation disambiguation, and 5.5~9% for natural language inference. Our approach also outperformed a previously developed prompt-based machine reading comprehension (MRC) model, GatorTron-MRC, for clinical concept and relation extraction. The proposed approach can deliver the ``one model for all`` promise from training to deployment using a unified generative LLM.

cs.CL↗

arXiv2023

Control4D: Efficient 4D Portrait Editing with Text

We introduce Control4D, an innovative framework for editing dynamic 4D portraits using text instructions. Our method addresses the prevalent challenges in 4D editing, notably the inefficiencies of existing 4D representations and the inconsistent editing effect caused by diffusion-based editors. We first propose GaussianPlanes, a novel 4D representation that makes Gaussian Splatting more structured by applying plane-based decomposition in 3D space and time. This enhances both efficiency and robustness in 4D editing. Furthermore, we propose to leverage a 4D generator to learn a more continuous generation space from inconsistent edited images produced by the diffusion-based editor, which effectively improves the consistency and quality of 4D editing. Comprehensive evaluation demonstrates the superiority of Control4D, including significantly reduced training time, high-quality rendering, and spatial-temporal consistency in 4D portrait editing. The link to our project website is https://control4darxiv.github.io.

cs.CV↗

arXiv2023

Nanoscale confinement and control of excitonic complexes in a monolayer WSe2

Nanoscale control and observation of photophysical processes in semiconductors is critical for basic understanding and applications from optoelectronics to quantum information processing. In particular, there are open questions and opportunities in controlling excitonic complexes in two-dimensional materials such as excitons, trions or biexcitons. However, neither conventional diffraction-limited optical spectroscopy nor lithography-limited electric control provides a proper tool to investigate these quasiparticles at the nanometer-scale at cryogenic temperature. Here, we introduce a cryogenic capacitive confocal optical microscope (C3OM) as a tool to study quasiparticle dynamics at the nanometer scale. Using a conductive atomic force microscope (AFM) tip as a gate electrode, we can modulate the electronic doping at the nanometer scale in WSe2 at 4K. This tool allows us to modulate with nanometer-scale confinement the exciton and trion peaks, as well a distinct photoluminescence line associated with a larger excitonic complex that exhibits distinctive nonlinear optical response. Our results demonstrate nanoscale confinement and spectroscopy of exciton complexes at arbitrary positions, which should prove an important tool for quantitative understanding of complex optoelectronic properties in semiconductors as well as for applications ranging from quantum spin liquids to superresolution measurements to control of quantum emitters.

cond-mat.mes-hall↗

arXiv2023

Multifield tunable valley splitting in two-dimensional MXene Cr$_2$COOH

Manipulation of the valley degree of freedom provides a novel paradigm in quantum information technology. Here, through first-principles calculations and model analysis, we demonstrate that monolayer Cr$_2$COOH MXene is a promising candidate material for valleytronics applications. We reveal that Cr$_2$COOH is a ferromagnetic semiconductor and harbors valley features. Due to the simultaneous breaking inversion symmetry and time-reversal symmetry, the valleys are polarized spontaneously. Moreover, the valley polarization is sizeable in both the valence and conduction bands, benefiting the observation of the anomalous valley Hall effect. More remarkably, the valley splitting can be effectively tuned by the magnetization direction, strain and ferroelectric substrate. More interestingly, the ferroelectric substrate Sc$_2$CO$_2$ can not only regulate the MAE, but also tune valley polarization state. Our findings offer a practical way for realizing highly tunable valleys by multiferroic couplings.

cond-mat.mtrl-sci↗