arXiv subjects

Nuo Chen

Publications and source records attributed to Nuo Chen.

At least 127 records · Page 7Linked to original sources

arXiv2024

SSCBench: A Large-Scale 3D Semantic Scene Completion Benchmark for Autonomous Driving

Monocular scene understanding is a foundational component of autonomous systems. Within the spectrum of monocular perception topics, one crucial and useful task for holistic 3D scene understanding is semantic scene completion (SSC), which jointly completes semantic information and geometric details from RGB input. However, progress in SSC, particularly in large-scale street views, is hindered by the scarcity of high-quality datasets. To address this issue, we introduce SSCBench, a comprehensive benchmark that integrates scenes from widely used automotive datasets (e.g., KITTI-360, nuScenes, and Waymo). SSCBench follows an established setup and format in the community, facilitating the easy exploration of SSC methods in various street views. We benchmark models using monocular, trinocular, and point cloud input to assess the performance gap resulting from sensor coverage and modality. Moreover, we have unified semantic labels across diverse datasets to simplify cross-domain generalization testing. We commit to including more datasets and SSC models to drive further advancements in this field.

cs.CV↗

arXiv2024

ControlMath: Controllable Data Generation Promotes Math Generalist Models

Utilizing large language models (LLMs) for data augmentation has yielded encouraging results in mathematical reasoning. However, these approaches face constraints in problem diversity, potentially restricting them to in-domain/distribution data generation. To this end, we propose ControlMath, an iterative method involving an equation-generator module and two LLM-based agents. The module creates diverse equations, which the Problem-Crafter agent then transforms into math word problems. The Reverse-Agent filters and selects high-quality data, adhering to the "less is more" principle, achieving better results with fewer data points. This approach enables the generation of diverse math problems, not limited to specific domains or distributions. As a result, we collect ControlMathQA, which involves 190k math word problems. Extensive results prove that combining our dataset with in-domain datasets like GSM8K can help improve the model's mathematical ability to generalize, leading to improved performances both within and beyond specific domains.

cs.LG↗

arXiv2024

MLLM-Bench: Evaluating Multimodal LLMs with Per-sample Criteria

Multimodal large language models (MLLMs) have broadened the scope of AI applications. Existing automatic evaluation methodologies for MLLMs are mainly limited in evaluating queries without considering user experiences, inadequately addressing the nuances of creative and associative multimodal tasks. However, the open-ended and subjective nature of such tasks poses a significant challenge to the evaluation methodology, where it is difficult to define the ground-truth answers for them. To this end, in our paper, we propose a new evaluation paradigm for MLLMs, which is evaluating MLLMs with per-sample criteria using potent MLLM as the judge. To validate the feasibility and effectiveness of this paradigm, we design a benchmark, dubbed MLLM-Bench, by curating the evaluation samples across six comprehensive cognitive levels. We benchmark 21 popular MLLMs in a pairwise-comparison fashion, showing diverse performance across models. Moreover, the validity of our benchmark manifests itself in reaching 88.02% agreement with human evaluation. We contend that the proposed paradigm explores the potential of MLLMs as effective evaluation tools with the help of per-sample criteria. See online leaderboard at \url{https://mllm-bench.llmzoo.com}.

cs.CL↗

arXiv2024

Cross-Domain Foundation Model Adaptation: Pioneering Computer Vision Models for Geophysical Data Analysis

We explore adapting foundation models (FMs) from the computer vision domain to geoscience. FMs, large neural networks trained on massive datasets, excel in diverse tasks with remarkable adaptability and generality. However, geoscience faces challenges like lacking curated training datasets and high computational costs for developing specialized FMs. This study considers adapting FMs from computer vision to geoscience, analyzing their scale, adaptability, and generality for geoscientific data analysis. We introduce a workflow that leverages existing computer vision FMs, fine-tuning them for geoscientific tasks, reducing development costs while enhancing accuracy. Through experiments, we demonstrate this workflow's effectiveness in broad applications to process and interpret geoscientific data of lunar images, seismic data, DAS arrays and so on. Our findings introduce advanced ML techniques to geoscience, proving the feasibility and advantages of cross-domain FMs adaptation, driving further advancements in geoscientific data analysis and offering valuable insights for FMs applications in other scientific domains.

cs.CV↗

arXiv2024

Room-Temperature Multiferroic Skyrmions in LiNbO3 with enhancement in electric-optical property

LiNbO3 (LN) is renowned for its exceptional ferroelectric properties, particularly its notable linear electro-optical (EO) effect, which is highly advantageous for various applications such as high-speed communication, optical computation, and quantum information processing. Compared to its ferroelectric properties, the magnetism of LN is not attractive enough due to its weak ferromagnetic nature. Theoretical studies suggest that LN may exhibit a novel magnetoelectric coupling via ferroelectrically-induced ferromagnetism. However, this mechanism has not yet been experimentally validated in any materials, presenting significant challenges for research. In this study, we provide the first experimental evidence supporting the mechanism of ferroelectrically-induced ferromagnetism in LN, including observations of the Dzyaloshinskii-Moriya interaction (DMI) and magnetoelectric coupling. Additionally, we have identified various multiferroic skyrmions, within which ferroelectric polarization signals are detectable. These signals can be influenced by the magnetic vortex structures, indicating a magnetoelectric coupling nature. Currently, they are the only multiferroic skyrmions that can keep stable at room temperature. Moreover, these magnetic textures significantly affect the ferroelectric properties, as demonstrated by an enhancement of the linear electro-optic effect of LN by over 200%. Given the novel magnetoelectric coupling mechanism, the potential of multiferroic skyrmions in spintronics and advanced data storage, and the extensive use of LN EO modulators, our research has significant implications for condensed matter physics, multiferroic materials, and optoelectronics.

cond-mat.mtrl-sci↗

arXiv2024

Vision-Based Dexterous Motion Planning by Dynamic Movement Primitives with Human Hand Demonstration

This paper proposes a vision-based framework for a 7-degree-of-freedom robotic manipulator, with the primary objective of facilitating its capacity to acquire information from human hand demonstrations for the execution of dexterous pick-and-place tasks. Most existing works only focus on the position demonstration without considering the orientations. In this paper, by employing a single depth camera, MediaPipe is applied to generate the three-dimensional coordinates of a human hand, thereby comprehensively recording the hand's motion, encompassing the trajectory of the wrist, orientation of the hand, and the grasp motion. A mean filter is applied during data pre-processing to smooth the raw data. The demonstration is designed to pick up an object at a specific angle, navigate around obstacles in its path and subsequently, deposit it within a sloped container. The robotic system demonstrates its learning capabilities, facilitated by the implementation of Dynamic Movement Primitives, enabling the assimilation of user actions into its trajectories with different start and end poi

cs.RO↗

arXiv2024

Stable room-temperature multiferroic skyrmions in lithium niobate with enhanced Pockels effect

Lithium Niobate (LN) is a ferroelectric material with exceptional electrical characteristics, including high piezoelectricity, high Pockels effect, etc. These properties make it a promising platform for numerous fields such as high-speed communication, optical computation, and quantum information processing. Besides these, the introduction of magnetic structures to LN holds significant potential to achieve magnetoelectric coupling, which can be applied in magnetic memory and data-processing devices with high efficiency. Here, for the first time, we observe a special topological magnetic structure called magnetic skyrmion in LN (SK-LN) by the combination of magnetic field annealing and rapid annealing processes . Compared to the magnetic skyrmions reported in magnetic systems, SK-LN exhibit exceptionally high stability. Additionally, the center of the magnetic vortex exhibits spontaneous ferroelectric polarization, indicating its multiferroic characteristic. With the excitation of these multiferroic skyrmions, the modulation efficiency of the electro-optical (EO) modulator fabricated on thin film lithium niobate on insulator (LNOI) wafer was found to be enhanced from 1.98 V*cm to 0.63 V*cm. It is considered that the multiferroic skyrmions significantly enhance the Pockels coefficient of LN to 101 pm/V, nearly three times the result (32pm/V) reported previously.

cond-mat.mtrl-sci↗

arXiv2024

GraphWiz: An Instruction-Following Language Model for Graph Problems

Large language models (LLMs) have achieved impressive success across several fields, but their proficiency in understanding and resolving complex graph problems is less explored. To bridge this gap, we introduce GraphInstruct, a novel and comprehensive instruction-tuning dataset designed to equip language models with the ability to tackle a broad spectrum of graph problems using explicit reasoning paths. Utilizing GraphInstruct, we build GraphWiz, an open-source language model capable of resolving various graph problem types while generating clear reasoning processes. To enhance the model's capability and reliability, we incorporate the Direct Preference Optimization (DPO) framework into the graph problem-solving context. The enhanced model, GraphWiz-DPO, achieves an average accuracy of 65% across nine tasks with different complexity levels, surpassing GPT-4 which has an average accuracy of 43.8%. Moreover, our research delves into the delicate balance between training data volume and model performance, highlighting the potential for overfitting with increased data. We also explore the transferability of the model's reasoning ability across different graph tasks, indicating the model's adaptability and practical application potential. Our investigation offers a new blueprint and valuable insights for developing LLMs specialized in graph reasoning and problem-solving.

cs.CL↗

arXiv2024

Compress to Impress: Unleashing the Potential of Compressive Memory in Real-World Long-Term Conversations

Existing retrieval-based methods have made significant strides in maintaining long-term conversations. However, these approaches face challenges in memory database management and accurate memory retrieval, hindering their efficacy in dynamic, real-world interactions. This study introduces a novel framework, COmpressive Memory-Enhanced Dialogue sYstems (COMEDY), which eschews traditional retrieval modules and memory databases. Instead, COMEDY adopts a "One-for-All" approach, utilizing a single language model to manage memory generation, compression, and response generation. Central to this framework is the concept of compressive memory, which intergrates session-specific summaries, user-bot dynamics, and past events into a concise memory format. To support COMEDY, we curated a large-scale Chinese instruction-tuning dataset, Dolphin, derived from real user-chatbot interactions. Comparative evaluations demonstrate COMEDY's superiority over traditional retrieval-based methods in producing more nuanced and human-like conversational experiences. Our codes are available at https://github.com/nuochenpku/COMEDY.

cs.CL↗

arXiv2024

Multiagent Multitraversal Multimodal Self-Driving: Open MARS Dataset

Large-scale datasets have fueled recent advancements in AI-based autonomous vehicle research. However, these datasets are usually collected from a single vehicle's one-time pass of a certain location, lacking multiagent interactions or repeated traversals of the same place. Such information could lead to transformative enhancements in autonomous vehicles' perception, prediction, and planning capabilities. To bridge this gap, in collaboration with the self-driving company May Mobility, we present the MARS dataset which unifies scenarios that enable MultiAgent, multitraveRSal, and multimodal autonomous vehicle research. More specifically, MARS is collected with a fleet of autonomous vehicles driving within a certain geographical area. Each vehicle has its own route and different vehicles may appear at nearby locations. Each vehicle is equipped with a LiDAR and surround-view RGB cameras. We curate two subsets in MARS: one facilitates collaborative driving with multiple vehicles simultaneously present at the same location, and the other enables memory retrospection through asynchronous traversals of the same location by multiple vehicles. We conduct experiments in place recognition and neural reconstruction. More importantly, MARS introduces new research opportunities and challenges such as multitraversal 3D reconstruction, multiagent perception, and unsupervised object discovery. Our data and codes can be found at https://ai4ce.github.io/MARS/.

cs.CV↗

arXiv2024

Towards unveiling the Cosmic Reionization: the ionizing photon production efficiency ($ξ_{ion}$) of Low-mass H$α$ emitters at $z \sim 2.3$

We investigate the galaxy properties of $\sim$400 low-mass ($<10^9\,M_{\odot}$) H$α$ emitters (HAEs) at z $\sim$ 2.3 in the ZFOURGE survey. The selection of these HAEs is based on the excess in the observed $K_s$ broad-band flux compared to the stellar continuum estimated from the best-fit SED. These low-mass HAEs have elevated SFR(H$α$) above the star formation main sequence (SFMS), making them potential analogs of the galaxies that reionized the universe during the epoch of reionization. The ionizing photon production efficiencies ($ξ_{ion}$) of the low-mass HAEs have a median value of $\mathrm{log}(ξ_{ion}/erg^{-1} Hz)=25.24^{+0.10}_{-0.13}\ (25.35^{+0.12}_{-0.15})$, assuming the Calzetti (SMC) curve for the stellar continuum dust correction. This value is higher than that of main sequence galaxies by $\sim$0.2 dex at similar redshift, indicating that the low-mass HAEs are more efficient in producing ionizing photons. Our results also consolidate the trend of increasing $ξ_{ion}$ with redshift, but reveal a "downsizing" relationship between $ξ_{ion}$ and stellar mass ($M_{\odot}$) with increasing redshift. We further explore the dependence of $ξ_{ion}$ on other galaxy properties, such as the UV spectral slope ($β_{\mathrm{UV}}$), the UV magnitude ($M_{\mathrm{UV}}$), the equivalent widths ($EWs$) of H$α$ and [O{\sc iii}] emission lines. Galaxies with the bluer UV slopes, fainter UV luminosities and higher equivalent widths exhibit elevated $ξ_{ion}$ by a factor of $\sim$2 compared to the median $ξ_{ion}$ of our sample. JWST data will provide an opportunity to extend our method and further investigate the properties of low-mass galaxies at high redshifts.

astro-ph.GA↗

arXiv2024

ChatDev: Communicative Agents for Software Development

Software development is a complex task that necessitates cooperation among multiple members with diverse skills. Numerous studies used deep learning to improve specific phases in a waterfall model, such as design, coding, and testing. However, the deep learning model in each phase requires unique designs, leading to technical inconsistencies across various phases, which results in a fragmented and ineffective development process. In this paper, we introduce ChatDev, a chat-powered software development framework in which specialized agents driven by large language models (LLMs) are guided in what to communicate (via chat chain) and how to communicate (via communicative dehallucination). These agents actively contribute to the design, coding, and testing phases through unified language-based communication, with solutions derived from their multi-turn dialogues. We found their utilization of natural language is advantageous for system design, and communicating in programming language proves helpful in debugging. This paradigm demonstrates how linguistic communication facilitates multi-agent collaboration, establishing language as a unifying bridge for autonomous task-solving among LLM agents. The code and data are available at https://github.com/OpenBMB/ChatDev.

cs.SE↗

arXiv2024

TransCoder: Towards Unified Transferable Code Representation Learning Inspired by Human Skills

Code pre-trained models (CodePTMs) have recently demonstrated a solid capacity to process various software intelligence tasks, e.g., code clone detection, code translation, and code summarization. The current mainstream method that deploys these models to downstream tasks is to fine-tune them on individual tasks, which is generally costly and needs sufficient data for large models. To tackle the issue, in this paper, we present TransCoder, a unified Transferable fine-tuning strategy for Code representation learning. Inspired by human inherent skills of knowledge generalization, TransCoder drives the model to learn better code-related meta-knowledge like human programmers. Specifically, we employ a tunable prefix encoder as the meta-learner to capture cross-task and cross-language transferable knowledge, respectively. Besides, tasks with minor training sample sizes and languages with small corpus can be remarkably benefited from our approach. Extensive experiments conducted on benchmark datasets clearly demonstrate that our method can lead to superior performance on various code-related tasks and encourage mutual reinforcement. We also show that TransCoder is applicable in low-resource scenarios. Our codes are available at https://github.com/QiushiSun/TransCoder.

cs.SE↗

arXiv2024

Make Prompt-based Black-Box Tuning Colorful: Boosting Model Generalization from Three Orthogonal Perspectives

Large language models (LLMs) have shown increasing power on various natural language processing (NLP) tasks. However, tuning these models for downstream tasks usually needs exorbitant costs or is unavailable due to commercial considerations. Recently, black-box tuning has been proposed to address this problem by optimizing task-specific prompts without accessing the gradients and hidden representations. However, most existing works have yet fully exploited the potential of gradient-free optimization under the scenario of few-shot learning. In this paper, we describe BBT-RGB, a suite of straightforward and complementary techniques for enhancing the efficiency and performance of black-box optimization. Specifically, our method includes three plug-and-play components: (1) Two-stage derivative-free optimization strategy that facilitates fast convergence and mitigates overfitting; (2) Automatic verbalizer construction with its novel usage under few-shot settings; (3) Better prompt initialization policy based on instruction search and auto-selected demonstration. Extensive experiments across various tasks on natural language understanding and inference demonstrate the effectiveness of our method. Our codes are publicly available at https://github.com/QiushiSun/BBT-RGB.

cs.CL↗

arXiv2024

Vector Quantization for Recommender Systems: A Review and Outlook

Vector quantization, renowned for its unparalleled feature compression capabilities, has been a prominent topic in signal processing and machine learning research for several decades and remains widely utilized today. With the emergence of large models and generative AI, vector quantization has gained popularity in recommender systems, establishing itself as a preferred solution. This paper starts with a comprehensive review of vector quantization techniques. It then explores systematic taxonomies of vector quantization methods for recommender systems (VQ4Rec), examining their applications from multiple perspectives. Further, it provides a thorough introduction to research efforts in diverse recommendation scenarios, including efficiency-oriented approaches and quality-oriented approaches. Finally, the survey analyzes the remaining challenges and anticipates future trends in VQ4Rec, including the challenges associated with the training of vector quantization, the opportunities presented by large language models, and emerging trends in multimodal recommender systems. We hope this survey can pave the way for future researchers in the recommendation community and accelerate their exploration in this promising field.

cs.IR↗

arXiv2024

Structure-aware Fine-tuning for Code Pre-trained Models

Over the past few years, we have witnessed remarkable advancements in Code Pre-trained Models (CodePTMs). These models achieved excellent representation capabilities by designing structure-based pre-training tasks for code. However, how to enhance the absorption of structural knowledge when fine-tuning CodePTMs still remains a significant challenge. To fill this gap, in this paper, we present Structure-aware Fine-tuning (SAT), a novel structure-enhanced and plug-and-play fine-tuning method for CodePTMs. We first propose a structure loss to quantify the difference between the information learned by CodePTMs and the knowledge extracted from code structure. Specifically, we use the attention scores extracted from Transformer layer as the learned structural information, and the shortest path length between leaves in abstract syntax trees as the structural knowledge. Subsequently, multi-task learning is introduced to improve the performance of fine-tuning. Experiments conducted on four pre-trained models and two generation tasks demonstrate the effectiveness of our proposed method as a plug-and-play solution. Furthermore, we observed that SAT can benefit CodePTMs more with limited training data.

cs.SE↗

arXiv2024

Multiple emission lines of H$α$ emitters at $z \sim 2.3$ from the broad and medium-band photometry in the ZFOURGE Survey

We present a multiple emission lines study of $\sim$1300 H$α$ emitters (HAEs) at $z \sim 2.3$ in the ZFOURGE survey. In contrast to the traditional spectroscopic method, our sample is selected based on the flux excess in the ZFOURGE-$K_s$ broad-band data relative to the best-fit stellar continuum. Using the same method, we also extract the strong diagnostic emission lines for these individual HAEs: [OIII]$λ\lambda4959,5007$, [OII]$λ\lambda3726,3729$. Our measurements exhibit good consistency with those obtained from spectroscopic surveys. We investigate the relationship between the equivalent widths (EWs) of these emission lines and various galaxy properties, including stellar mass, stellar age, star formation rate (SFR), specific SFR (sSFR), ionization states (O32). We have identified a discrepancy between between HAEs at $z\sim2.3$ and typical local star-forming galaxy observed in the SDSS, suggesting the evolution of lower gas-phase metallicity ($Z$) and higher ionization parameters ($U$) with redshift. Notably, we have observed a significant number of low-mass HAEs exhibiting exceptionally high $EW_{\mathrm{[OIII]}}$. Their galaxy properties are comparable to those of extreme objects, such as extreme O3 emitters (O3Es) and Ly$α$ emitters (LAEs) at $z\simeq2-3$. Considering that these characteristics may indicate potential strong Lyman continuum (LyC) leakage, higher redshift anaglogs of the low-mass HAEs could be significant contributors to the cosmic reionization. Further investigations on this particular population are required to gain a clearer understanding of galaxy evolution and cosmic reionization.

astro-ph.GA↗

arXiv2024

Development of a near-infrared wide-field integral field unit by ultra-precision diamond cutting

Integral Field Spectroscopy (IFS) is an observational method to obtain spatially resolved spectra over a specific field of view (FoV) in a single exposure. In recent years, near-infrared IFS has gained importance in observing objects with strong dust attenuation or at high redshift. One limitation of existing near-infrared IFS instruments is their relatively small FoV, less than 100 arcsec$^2$, compared to optical instruments. Therefore, we have developed a near-infrared (0.9-2.5 $\mathrmμ$m) image-slicer type integral field unit (IFU) with a larger FoV of 13.5 $\times$ 10.4 arcsec$^2$ by matching a slice width to a typical seeing size of 0.4 arcsec. The IFU has a compact optical design utilizing off-axis ellipsoidal mirrors to reduce aberrations. Complex optical elements were fabricated using an ultra-precision cutting machine to achieve RMS surface roughness of less than 10 nm and a P-V shape error of less than 300 nm. The ultra-precision machining can also simplify alignment procedures. The on-sky performance evaluation confirmed that the image quality and the throughput of the IFU were as designed. In conclusion, we have successfully developed a compact IFU utilizing an ultra-precision cutting technique, almost fulfilling the requirements.

astro-ph.IM↗