arXiv subjects

Guo Chen

Publications and source records attributed to Guo Chen.

At least 91 records · Page 5Linked to original sources

arXiv2025

Modeling Fine-Grained Hand-Object Dynamics for Egocentric Video Representation Learning

In egocentric video understanding, the motion of hands and objects as well as their interactions play a significant role by nature. However, existing egocentric video representation learning methods mainly focus on aligning video representation with high-level narrations, overlooking the intricate dynamics between hands and objects. In this work, we aim to integrate the modeling of fine-grained hand-object dynamics into the video representation learning process. Since no suitable data is available, we introduce HOD, a novel pipeline employing a hand-object detector and a large language model to generate high-quality narrations with detailed descriptions of hand-object dynamics. To learn these fine-grained dynamics, we propose EgoVideo, a model with a new lightweight motion adapter to capture fine-grained hand-object motion information. Through our co-training strategy, EgoVideo effectively and efficiently leverages the fine-grained hand-object dynamics in the HOD data. Extensive experiments demonstrate that our method achieves state-of-the-art performance across multiple egocentric downstream tasks, including improvements of 6.3% in EK-100 multi-instance retrieval, 5.7% in EK-100 classification, and 16.3% in EGTEA classification in zero-shot settings. Furthermore, our model exhibits robust generalization capabilities in hand-object interaction and robot manipulation tasks. Code and data are available at https://github.com/OpenRobotLab/EgoHOD/.

cs.CV↗

arXiv2025

Realizing stable zig-zag polymeric nitrogen chains in P-N compounds

The zig-zag Nitrogen (N) chain similar to the Ch-N structure has long been considered a potential high energy density structure. However, all previously predicted zig-zag N chain structures similar to Ch-N exhibit imaginary frequencies in their phonon spectra at 0 GPa. Here, we conducted a systematic investigation of P-N compounds using first-principles calculations, uncovering a series of structurally similar stable phases, C2/m-PNx (x = 6, 8, 10, 12, 14), in which N forms zig-zag N chains similar to those in Ch-N. In P-N compounds, the longest zig-zag N chain that can theoretically remain stable under ambient pressure is the N chain composed of 14 N atoms in C2/m-PN14. If the N chain continues to grow, inter-chain vibrational imaginary frequencies will arise in the system. Notably, N chains with an even number of atoms are more likely to be energetically favorable. The five C2/m-PNx phases and one metastable phase (R-PN6) exhibit both remarkable stability and excellent detonability at ambient pressure, positioning them as promising candidates for high-energy-density materials. In addition, the R-PN6 is the first structure to stabilize the N6 ring through covalent bonding, with the covalent network contributing to its high hardness (47.59 GPa).

cond-mat.mtrl-sci↗

arXiv2025

Spatial-offset pump-probe imaging of nonradiative dynamics at optical resolution

Nonradiative photothermal (PT) and photoacoustic (PA) processes have found widespread applications in imaging, stimulation, and therapy. Mapping the generation and propagation of PA and PT waves with resolution is important to elucidate how these fields interact with biological systems. To this end, we introduce spatial offset pump-probe imaging (SOPPI). By spatially offsetting the pump beam and the probe beam, SOPPI can image simultaneously PA and PT wave propagation with nanosecond temporal resolution, micrometer spatial resolution, 65 MHz detection bandwidth, and a sensitivity of 9.9 Pa noise equivalent pressure. We first map the PA and PT evolution from a fiber emitter, and how the wave interacting with a mouse skull and brain slices. SOPPI imaging of PA waves from a tapered fiber with water as an absorber shows a wavelength-dependent generation, evanescent wave generated PA, and back-propagated acoustic Mach Cone. At last, a SOPPI-PACT is developed to reconstruct the pigment distribution inside a zebrafish larva with high precision and signal-to-noise ratio.

physics.optics↗

arXiv2025

Eagle 2: Building Post-Training Data Strategies from Scratch for Frontier Vision-Language Models

Recently, promising progress has been made by open-source vision-language models (VLMs) in bringing their capabilities closer to those of proprietary frontier models. However, most open-source models only publish their final model weights, leaving the critical details of data strategies and implementation largely opaque. In this work, we address VLM post-training from a data-centric perspective, showing the key role of data strategy in developing frontier VLMs. By studying and building our post-training data strategy from scratch, we share detailed insights into the development processes, aiming to benefit the development of competitive models for the open-source community. Our introduced data strategy, together with training recipes and model design, leads to a family of performant VLMs named Eagle2. Specifically, Eagle2-9B achieves state-of-the-art results across various multimodal benchmarks, matching certain competitive models with up to 70B parameters.

cs.CV↗

arXiv2024

Vinci: A Real-time Embodied Smart Assistant based on Egocentric Vision-Language Model

We introduce Vinci, a real-time embodied smart assistant built upon an egocentric vision-language model. Designed for deployment on portable devices such as smartphones and wearable cameras, Vinci operates in an "always on" mode, continuously observing the environment to deliver seamless interaction and assistance. Users can wake up the system and engage in natural conversations to ask questions or seek assistance, with responses delivered through audio for hands-free convenience. With its ability to process long video streams in real-time, Vinci can answer user queries about current observations and historical context while also providing task planning based on past interactions. To further enhance usability, Vinci integrates a video generation module that creates step-by-step visual demonstrations for tasks that require detailed guidance. We hope that Vinci can establish a robust framework for portable, real-time egocentric AI systems, empowering users with contextual and actionable insights. We release the complete implementation for the development of the device in conjunction with a demo web platform to test uploaded videos at https://github.com/OpenGVLab/vinci.

cs.CV↗

arXiv2024

EF-LLM: Energy Forecasting LLM with AI-assisted Automation, Enhanced Sparse Prediction, Hallucination Detection

Accurate prediction helps to achieve supply-demand balance in energy systems, supporting decision-making and scheduling. Traditional models, lacking AI-assisted automation, rely on experts, incur high costs, and struggle with sparse data prediction. To address these challenges, we propose the Energy Forecasting Large Language Model (EF-LLM), which integrates domain knowledge and temporal data for time-series forecasting, supporting both pre-forecast operations and post-forecast decision-support. EF-LLM's human-AI interaction capabilities lower the entry barrier in forecasting tasks, reducing the need for extra expert involvement. To achieve this, we propose a continual learning approach with updatable LoRA and a multi-channel architecture for aligning heterogeneous multimodal data, enabling EF-LLM to continually learn heterogeneous multimodal knowledge. In addition, EF-LLM enables accurate predictions under sparse data conditions through its ability to process multimodal data. We propose Fusion Parameter-Efficient Fine-Tuning (F-PEFT) method to effectively leverage both time-series data and text for this purpose. EF-LLM is also the first energy-specific LLM to detect hallucinations and quantify their occurrence rate, achieved via multi-task learning, semantic similarity analysis, and ANOVA. We have achieved success in energy prediction scenarios for load, photovoltaic, and wind power forecast.

cs.LG↗

arXiv2024

CG-Bench: Clue-grounded Question Answering Benchmark for Long Video Understanding

Most existing video understanding benchmarks for multimodal large language models (MLLMs) focus only on short videos. The limited number of benchmarks for long video understanding often rely solely on multiple-choice questions (MCQs). However, because of the inherent limitation of MCQ-based evaluation and the increasing reasoning ability of MLLMs, models can give the current answer purely by combining short video understanding with elimination, without genuinely understanding the video content. To address this gap, we introduce CG-Bench, a novel benchmark designed for clue-grounded question answering in long videos. CG-Bench emphasizes the model's ability to retrieve relevant clues for questions, enhancing evaluation credibility. It features 1,219 manually curated videos categorized by a granular system with 14 primary categories, 171 secondary categories, and 638 tertiary categories, making it the largest benchmark for long video analysis. The benchmark includes 12,129 QA pairs in three major question types: perception, reasoning, and hallucination. Compensating the drawbacks of pure MCQ-based evaluation, we design two novel clue-based evaluation methods: clue-grounded white box and black box evaluations, to assess whether the model generates answers based on the correct understanding of the video. We evaluate multiple closed-source and open-source MLLMs on CG-Bench. Results indicate that current models significantly underperform in understanding long videos compared to short ones, and a significant gap exists between open-source and commercial models. We hope CG-Bench can advance the development of more trustworthy and capable MLLMs for long video understanding. All annotations and video data are released at https://cg-bench.github.io/leaderboard/.

cs.CV↗

arXiv2024

High-pressure synthesis of K_{4}N_{6} compound entirely composed of aromatic hexazine [N_{6}]^{4-} anion

The synthesis of hexazine N_{6} ring is another milestone in nitrogen chemistry after that of aromatic [N_{5}]^{-} anion. However, due to the diversity of carried charges, realizing compounds entirely composed of aromatic hexazine N_{6} ring potentially with high-stability is a challenge. The first reported hexazine N_{6} ring is [N_{6}]^{2-} anion in K_{2}N_{6} [Nat. Chem. 14, 794 (2022)] that does not adhere to H\''uckel's rule, and subsequently, the aromatic hexazine [N_{6}]^{4-} anion mixed with [N_{5}]^{-} anion and N_{2} dimers is realized in the complex compound K_{9}N_{56} [Nat. Chem. 15, 641 (2023)], where 5.36\% of N atoms form aromatic N_{6} ring. Here, we theoretically predict that all N atoms form aromatic hexazine [N_{6}]^{4-} anion in K_{4}N_{6}, which becomes stable at 60 GPa and can stably exist up to 600 K at 0 GPa. Following this approach, based on the diamond anvil cell, K_{4}N_{6} composed of 100\% aromatic hexazine [N_{6}]^{4-} anion is synthesized at 45 GPa after laser-heating and identified by synchrotron X-ray diffraction and Raman spectroscopy. Our results bring us closer to achieving aromatic N6 rings at ambient condition.

cond-mat.mtrl-sci↗

arXiv2024

The Terminator Region Atmosphere of the hot Jupiter WASP-77Ab with ESPRESSO/VLT observations

Atmospheric studies are essential for elucidating the formation history, evolutionary processes, and atmospheric dynamics of exoplanets. High-resolution transmission spectroscopy offers the advantage of detecting subtle variations in stellar spectral profiles, thereby enabling the identification of the sources of observed signals. In this study, we present the transmission spectra of the exoplanet WASP-77Ab, a hot Jupiter with a 1.36-day orbital period around a G8 host star with $V=11.29$ mag. These observations were conducted using the high-resolution spectrograph ESPRESSO at the Very Large Telescope over three transit events. We analyze the Rossiter-McLaughlin effect for WASP-77A and determine a projected spin-orbit angle of ${λ= 16.131^{\circ}}^{+2.106}_{-2.324}$, indicating that the planet's orbit is nearly aligned. Following the generation of transmission spectra for the three nights, we model and correct for center-to-limb variation and the Rossiter-McLaughlin effects. In the residual transmission spectra, we detect H$α$, H$β$ and CaII H with a significance exceeding 3.5$σ$. After applying 0.1-0.5 Å masks to the cores of these lines to mitigate stellar contamination, all them still shows visible absorptions although not significant, suggesting at least partial planet contribution to them. Therefore, we are yet unable to confirm or reject the planetary origin of these spectral signals based on the current data set. Further investigation of WASP-77Ab's atmosphere, particularly in areas beyond the terminator region, is essential to illuminate the planet's two-dimensional atmospheric structure.

astro-ph.EP↗

arXiv2024

Charge transfer induced cubic gauche nitrogen from azides

Cubic gauche nitrogen (cg-N) with a three-dimensional network of N-N single bonds attracted lots of attentions in last decades, since it theoretically has five times larger energy than TNT. While, extreme environments of high pressure or plasma treatment were required in traditional routes. Quite recently, in vacuum or protective gas environments, a one-step synthesis route relying solely on heating is reported giving the highest cg-N content. However, corresponding mechanism is missing, which hinders the improvement of yield and the development of simpler methods. Here, by treating KN3 in different gas environments, we find that moisture can prevent the transition from KN3 to cg-N. In a dry air environment at 260 ~ 300°C, KN3 decomposes into K and N2, and charge transfer from K to KN3 can induce cg-N. Furthermore, by grinding or loading pressure on the mixture of KN3 with Li, Na, K, Cs, Ca, Mg and Al, we find that elements with higher electronegativity, higher pressure and temperature conditions are needed to induce cg-N, while grinding alone is sufficient for alkali metals even without heating, thus confirming the charge-transfer mechanism. These findings provide guidance for the synthesis of cg-N under ambient conditions through metal-catalyzed polymerization of various azides.

cond-mat.mtrl-sci↗

arXiv2024

One-step Synthesis of Cubic Gauche Polymeric Nitrogen with High Yield Just by Heating

A high-efficient one-step synthesis of cubic gauche polymeric nitrogen was developed just by thermal treatment of KN3 powders. The Raman and infrared spectra confirm the formation of polymeric nitrogen networks. Thermogravimetric differential scanning calorimeter measurements show that the content of cubic gauche polymeric nitrogen is as high as 1.5 wt% with high thermal stability, which is the highest content value so far.

cond-mat.mtrl-sci↗

arXiv2024

Thermodynamics of the $H^2 + H^{-2}$ Dark Energy model

In this study, we introduced a phenomenological dark energy model $H^{2}+H^{-2}$ model) that incorporates the first-order approximation of Kaniadakis holographic entropy dark energy and utilizes the Hubble horizon, $1/H$ as the infrared cutoff. The advantage of this model is that it can relieve the Hubble tension issue and cicumventing the potential issue of a "big rip". In this article, we will study the thermodynamics of the model and obtain the corrected temperature. In addition, we found that the model modified the entropy area relationship by adding an area cubic term in addition to the area term.

gr-qc↗

arXiv2024

Mid-infrared Energy Deposition Spectroscopy

Photothermal microscopy is an emerging tool for measuring light-matter interactions with single-molecule sensitivity. It is generally believed that the spectral acquisition speed in photothermal microscopy is limited by the slow thermal diffusion process. Here, we demonstrate mid-infrared energy deposition (MIRED) spectroscopy, which offers both microsecond-scale temporal resolution and sub-micron spatial resolution. In this approach, the photothermal process is optically probed while the infrared pulses from a quantum cascade laser array are rapidly tuned. Based on Newton's law, the energy deposition corresponds to the first derivative of local temperature rise over time and provides the instantaneous infrared absorption. By employing time-resolved measurement of transient energy deposition, the upper limit for spectrum encoding shifts to the vibrational relaxation level, which occurs on the picosecond scale. This method significantly increases the detection bandwidth while maintaining the sensitivity and resolution advantages of photothermal detection.

physics.optics↗

arXiv2024

SPMamba: State-space model is all you need in speech separation

Existing CNN-based speech separation models face local receptive field limitations and cannot effectively capture long time dependencies. Although LSTM and Transformer-based speech separation models can avoid this problem, their high complexity makes them face the challenge of computational resources and inference efficiency when dealing with long audio. To address this challenge, we introduce an innovative speech separation method called SPMamba. This model builds upon the robust TF-GridNet architecture, replacing its traditional BLSTM modules with bidirectional Mamba modules. These modules effectively model the spatiotemporal relationships between the time and frequency dimensions, allowing SPMamba to capture long-range dependencies with linear computational complexity. Specifically, the bidirectional processing within the Mamba modules enables the model to utilize both past and future contextual information, thereby enhancing separation performance. Extensive experiments conducted on public datasets, including WSJ0-2Mix, WHAM!, and Libri2Mix, as well as the newly constructed Echo2Mix dataset, demonstrated that SPMamba significantly outperformed existing state-of-the-art models, achieving superior results while also reducing computational complexity. These findings highlighted the effectiveness of SPMamba in tackling the intricate challenges of speech separation in complex environments.

cs.SD↗

arXiv2024

InternVideo2: Scaling Foundation Models for Multimodal Video Understanding

We introduce InternVideo2, a new family of video foundation models (ViFM) that achieve the state-of-the-art results in video recognition, video-text tasks, and video-centric dialogue. Our core design is a progressive training approach that unifies the masked video modeling, crossmodal contrastive learning, and next token prediction, scaling up the video encoder size to 6B parameters. At the data level, we prioritize spatiotemporal consistency by semantically segmenting videos and generating video-audio-speech captions. This improves the alignment between video and text. Through extensive experiments, we validate our designs and demonstrate superior performance on over 60 video and audio tasks. Notably, our model outperforms others on various video-related dialogue and long video understanding benchmarks, highlighting its ability to reason and comprehend longer contexts. Code and models are available at https://github.com/OpenGVLab/InternVideo/tree/main/InternVideo2/.

cs.CV↗

arXiv2024

Closeby Habitable Exoplanet Survey (CHES). II. An Observation Strategy for the Target Stars

The Closeby Habitable Exoplanet Survey (CHES) constitutes a mission intricately designed to systematically survey approximately 100 solar-type stars located within the immediate proximity of the solar system, specifically within a range of 10 parsecs. The core objective of this mission is the detection and characterization of potentially habitable Earth-like planets or super-Earths within the habitable zone of these stars. The CHES mission obtains high-precision astrometric measurements of planets orbiting the target stars by observing angular distance variations between the target star and reference stars. As a result, we surveyed the relevant parameters of both target and reference stars in detail, conducting a thorough analysis and calculation of the required observation accuracy, the number of observations, and the priority assigned to each target star. Observational emphasis will be concentrated on targets considered of higher priority, ensuring the effectiveness of their observation capabilities. Through this approach, we formulate a five-year observation strategy that will cover all the target stars within a six-month timeframe. The strategy not only fulfills the required observing capability but also exhibit high efficiency simultaneously, providing an executable program for future mission. Over the span of the mission's five-year duration, a cumulative observation time of 29,220 hours will be available. Approximately 86 percent of this, totaling 25,120 hours, is allocated for the observation of target stars. This allocation leaves approximately 4,100 hours for extended scientific observation programs. We have also performed simulated observations based on this strategy and verified its observational capability for exoplanets.

astro-ph.EP↗

arXiv2024

Large Language Model-aided Edge Learning in Distribution System State Estimation

Distribution system state estimation (DSSE) plays a crucial role in the real-time monitoring, control, and operation of distribution networks. Besides intensive computational requirements, conventional DSSE methods need high-quality measurements to obtain accurate states, whereas missing values often occur due to sensor failures or communication delays. To address these challenging issues, a forecast-then-estimate framework of edge learning is proposed for DSSE, leveraging large language models (LLMs) to forecast missing measurements and provide pseudo-measurements. Firstly, natural language-based prompts and measurement sequences are integrated by the proposed LLM to learn patterns from historical data and provide accurate forecasting results. Secondly, a convolutional layer-based neural network model is introduced to improve the robustness of state estimation under missing measurement. Thirdly, to alleviate the overfitting of the deep learning-based DSSE, it is reformulated as a multi-task learning framework containing shared and task-specific layers. The uncertainty weighting algorithm is applied to find the optimal weights to balance different tasks. The numerical simulation on the Simbench case is used to demonstrate the effectiveness of the proposed forecast-then-estimate framework.

eess.SY↗

arXiv2024

Evidence for Nightside Water Emission Found in Transit of Ultrahot Jupiter WASP-33b

To date, the dayside thermal structure of ultrahot Jupiters (UHJs) is generally considered to be inverted, but their nightside thermal structure has been less explored. Here we explore the impact of nightside thermal emission on high-resolution infrared transmission spectroscopy, which should not be neglected, especially for UHJs. We present a general equation for the high-resolution transmission spectrum that includes planetary nightside thermal emission. This provides a new way to infer the thermal structure of the planetary nightside with high-resolution transmission spectroscopy. Using the cross-correlation technique, we find evidence for the presence of an H$_2$O emission signature on the UHJ WASP-33b during the transit, indicating an inverted temperature structure on its nightside. Such a result suggests a stronger heat transport through the circulation than currently expected. An alternative explanation is that the rotating visible hemisphere during transit leads to the potential contribution of the limb and dayside atmospheres to the detected emission signature. In the future, the combination of high-resolution full-phase curve spectroscopic observations and general circulation models will hopefully solve this puzzle and provide a complete picture of the three-dimensional nature of the chemistry, circulation, and thermal structure of UHJs.

astro-ph.EP↗