Searcharxiv⌕ Search

arXiv subjects

Guang Yang

Publications and source records attributed to Guang Yang.

At least 325 records · Page 18Linked to original sources
arXiv2023

Online Control Barrier Functions for Decentralized Multi-Agent Navigation

Control barrier functions (CBFs) enable guaranteed safe multi-agent navigation in the continuous domain. The resulting navigation performance, however, is highly sensitive to the underlying hyperparameters. Traditional approaches consider fixed CBFs (where parameters are tuned apriori), and hence, typically do not perform well in cluttered and highly dynamic environments: conservative parameter values can lead to inefficient agent trajectories, or even failure to reach goal positions, whereas aggressive parameter values can lead to infeasible controls. To overcome these issues, in this paper, we propose online CBFs, whereby hyperparameters are tuned in real-time, as a function of what agents perceive in their immediate neighborhood. Since the explicit relationship between CBFs and navigation performance is hard to model, we leverage reinforcement learning to learn CBF-tuning policies in a model-free manner. Because we parameterize the policies with graph neural networks (GNNs), we are able to synthesize decentralized agent controllers that adjust parameter values locally, varying the degree of conservative and aggressive behaviors across agents. Simulations as well as real-world experiments show that (i) online CBFs are capable of solving navigation scenarios that are infeasible for fixed CBFs, and (ii), that they improve navigation performance by adapting to other agents and changes in the environment.

cs.RO↗
arXiv2023

CEERS Key Paper VIII: Emission Line Ratios from NIRSpec and NIRCam Wide-Field Slitless Spectroscopy at z>2

We use James Webb Space Telescope Near-Infrared Camera Wide Field Slitless Spectroscopy (NIRCam WFSS) and Near-Infrared spectrograph (NIRSpec) in the Cosmic Evolution Early Release survey (CEERS) to measure rest-frame optical emission-line of 155 galaxies at z>2. The blind NIRCam grism observations include a sample of galaxies with bright emission lines that were not observed on the NIRSpec masks. We study the changes of the Ha, [OIII]/Hb, and [NeIII]/[OII] emission lines in terms of redshift by comparing to lower redshift SDSS and CLEAR samples. We find a significant (>3$σ$) correlation between [OIII]/Hb with redshift, while [NeIII]/[OII] has a marginal (2$σ$) correlation with redshift. We compare [OIII]/Hb and [NeIII]/[OII] to stellar mass and Hb SFR. We find that both emission-line ratios have a correlation with Hb SFR and an anti-correlation with stellar mass across the redshifts 0<z<9. Comparison with MAPPINGS~V models indicates that these trends are consistent with lower metallicity and higher ionization in low-mass and high-SFR galaxies. We additionally compare to IllustriousTNG predictions and find that they effectively describe the highest [OIII]/Hb ratios observed in our sample, without the need to invoke MAPPINGS models with significant shock ionizionation components.

astro-ph.GA↗
arXiv2023

DMKD: Improving Feature-based Knowledge Distillation for Object Detection Via Dual Masking Augmentation

Recent mainstream masked distillation methods function by reconstructing selectively masked areas of a student network from the feature map of its teacher counterpart. In these methods, the masked regions need to be properly selected, such that reconstructed features encode sufficient discrimination and representation capability like the teacher feature. However, previous masked distillation methods only focus on spatial masking, making the resulting masked areas biased towards spatial importance without encoding informative channel clues. In this study, we devise a Dual Masked Knowledge Distillation (DMKD) framework which can capture both spatially important and channel-wise informative clues for comprehensive masked feature reconstruction. More specifically, we employ dual attention mechanism for guiding the respective masking branches, leading to reconstructed feature encoding dual significance. Furthermore, fusing the reconstructed features is achieved by self-adjustable weighting strategy for effective feature distillation. Our experiments on object detection task demonstrate that the student networks achieve performance gains of 4.1% and 4.3% with the help of our method when RetinaNet and Cascade Mask R-CNN are respectively used as the teacher networks, while outperforming the other state-of-the-art distillation methods.

cs.CV↗
arXiv2023

Optimization towards Efficiency and Stateful of dispel4py

Scientific workflows bridge scientific challenges with computational resources. While dispel4py, a stream-based workflow system, offers mappings to parallel enactment engines like MPI or Multiprocessing, its optimization primarily focuses on dynamic process-to-task allocation for improved performance. An efficiency gap persists, particularly with the growing emphasis on conserving computing resources. Moreover, the existing dynamic optimization lacks support for stateful applications and grouping operations. To address these issues, our work introduces a novel hybrid approach for handling stateful operations and groupings within workflows, leveraging a new Redis mapping. We also propose an auto-scaling mechanism integrated into dispel4py's dynamic optimization. Our experiments showcase the effectiveness of auto-scaling optimization, achieving efficiency while upholding performance. In the best case, auto-scaling reduces dispel4py's runtime to 87% compared to the baseline, using only 76% of process resources. Importantly, our optimized stateful dispel4py demonstrates a remarkable speedup, utilizing just 32% of the runtime compared to the contender.

cs.DC↗
arXiv2023

A CEERS Discovery of an Accreting Supermassive Black Hole 570 Myr after the Big Bang: Identifying a Progenitor of Massive z > 6 Quasars

We report the discovery of an accreting supermassive black hole at z=8.679, in CEERS_1019, a galaxy previously discovered via a Ly$α$-break by Hubble and with a Ly$α$ redshift from Keck. As part of the Cosmic Evolution Early Release Science (CEERS) survey, we observed this source with JWST/NIRSpec spectroscopy, MIRI and NIRCam imaging, and NIRCam/WFSS slitless spectroscopy. The NIRSpec spectra uncover many emission lines, and the strong [O III] emission line confirms the ground-based Ly$α$ redshift. We detect a significant broad (FWHM~1200 km/s) component in the H$β$ emission line, which we conclude originates in the broad-line region of an active galactic nucleus (AGN), as the lack of a broad component in the forbidden lines rejects an outflow origin. This hypothesis is supported by the presence of high-ionization lines, as well as a spatial point-source component embedded within a smoother surface brightness profile. The mass of the black hole is log($M_{BH}/M_{\odot})=6.95{\pm}0.37$, and we estimate that it is accreting at 1.2 ($\pm$0.5) x the Eddington limit. The 1-8 $μ$m photometric spectral energy distribution (SED) from NIRCam and MIRI shows a continuum dominated by starlight and constrains the host galaxy to be massive (log M/M$_{\odot}$~9.5) and highly star-forming (SFR~30 M$_{\odot}$ yr$^{-1}$). Ratios of the strong emission lines show that the gas in this galaxy is metal-poor (Z/Z$_{\odot}$~0.1), dense (n$_{e}$~10$^{3}$ cm$^{-3}$), and highly ionized (log U~-2.1), consistent with the general galaxy population observed with JWST at high redshifts. We use this presently highest-redshift AGN discovery to place constraints on black hole seeding models and find that a combination of either super-Eddington accretion from stellar seeds or Eddington accretion from massive black hole seeds is required to form this object by the observed epoch.

astro-ph.GA↗
arXiv2023

Video-Instrument Synergistic Network for Referring Video Instrument Segmentation in Robotic Surgery

Robot-assisted surgery has made significant progress, with instrument segmentation being a critical factor in surgical intervention quality. It serves as the building block to facilitate surgical robot navigation and surgical education for the next generation of operating intelligence. Although existing methods have achieved accurate instrument segmentation results, they simultaneously generate segmentation masks for all instruments, without the capability to specify a target object and allow an interactive experience. This work explores a new task of Referring Surgical Video Instrument Segmentation (RSVIS), which aims to automatically identify and segment the corresponding surgical instruments based on the given language expression. To achieve this, we devise a novel Video-Instrument Synergistic Network (VIS-Net) to learn both video-level and instrument-level knowledge to boost performance, while previous work only used video-level information. Meanwhile, we design a Graph-based Relation-aware Module (GRM) to model the correlation between multi-modal information (i.e., textual description and video frame) to facilitate the extraction of instrument-level information. We are also the first to produce two RSVIS datasets to promote related research. Our method is verified on these datasets, and experimental results exhibit that the VIS-Net can significantly outperform existing state-of-the-art referring segmentation methods. Our code and our datasets will be released upon the publication of this work.

cs.CV↗
arXiv2023

CEERS Key Paper VII: JWST/MIRI Reveals a Faint Population of Galaxies at Cosmic Noon Unseen by Spitzer

The Cosmic Evolution Early Release Science (CEERS) program observed the Extended Groth Strip with the Mid-Infrared Instrument (MIRI) on the James Webb Space Telescope (JWST) in 2022. In this paper, we discuss the four MIRI pointings that observed with longer wavelength filters, including F770W, F1000W, F1280W, F1500W, F1800W, and F2100W. We compare the MIRI galaxies with the Spitzer/MIPS 24$μ$m population in the EGS field. We find that MIRI can observe an order of magnitude deeper than MIPS in significantly shorter integration times, attributable to JWST's much larger aperture and MIRI's improved sensitivity. MIRI is exceptionally good at finding faint ($L_{\rm IR}<10^{10} L_\odot$) galaxies at $z\sim1-2$. We find that a significant portion of MIRI galaxies are "mid-IR weak"--they have strong near-IR emission and relatively weaker mid-IR emission, and most of the star formation is unobscured. We present new IR templates that capture how the mid-IR to near-IR emission changes with increasing infrared luminosity. We present two color-color diagrams to separate mid-IR weak galaxies and active galactic nuclei (AGN) from dusty star-forming galaxies and find that these color diagrams are most effective when used in conjunction with each other. We present the first number counts of 10$μ$m sources and find that there are $\lesssim10$ IR AGN per MIRI pointing, possibly due to the difficulty of distinguishing AGN from intrinsically mid-IR weak galaxies (due to low metallicities or low dust content). We conclude that MIRI is most effective at observing moderate luminosity ($L_{\rm IR}=10^9-10^{10}L_\odot$) galaxies at $z=1-2$, and that photometry alone is not effective at identifying AGN within this faint population.

astro-ph.GA↗
arXiv2023

How Important are Good Method Names in Neural Code Generation? A Model Robustness Perspective

Pre-trained code generation models (PCGMs) have been widely applied in neural code generation which can generate executable code from functional descriptions in natural languages, possibly together with signatures. Despite substantial performance improvement of PCGMs, the role of method names in neural code generation has not been thoroughly investigated. In this paper, we study and demonstrate the potential of benefiting from method names to enhance the performance of PCGMs, from a model robustness perspective. Specifically, we propose a novel approach, named RADAR (neuRAl coDe generAtor Robustifier). RADAR consists of two components: RADAR-Attack and RADAR-Defense. The former attacks a PCGM by generating adversarial method names as part of the input, which are semantic and visual similar to the original input, but may trick the PCGM to generate completely unrelated code snippets. As a countermeasure to such attacks, RADAR-Defense synthesizes a new method name from the functional description and supplies it to the PCGM. Evaluation results show that RADAR-Attack can reduce the CodeBLEU of generated code by 19.72% to 38.74% in three state-of-the-art PCGMs (i.e., CodeGPT, PLBART, and CodeT5) in the fine-tuning code generation task, and reduce the Pass@1 of generated code by 32.28% to 44.42% in three state-of-the-art PCGMs (i.e., Replit, CodeGen, and CodeT5+) in the zero-shot code generation task. Moreover, RADAR-Defense is able to reinstate the performance of PCGMs with synthesized method names. These results highlight the importance of good method names in neural code generation and implicate the benefits of studying model robustness in software engineering.

cs.SE↗
arXiv2023

A Syntax-Guided Multi-Task Learning Approach for Turducken-Style Code Generation

Due to the development of pre-trained language models, automated code generation techniques have shown great promise in recent years. However, the generated code is difficult to meet the syntactic constraints of the target language, especially in the case of Turducken-style code, where declarative code snippets are embedded within imperative programs. In this study, we summarize the lack of syntactic constraints into three significant challenges: (1) the efficient representation of syntactic constraints, (2) the effective integration of syntactic information, and (3) the scalable syntax-first decoding algorithm. To address these challenges, we propose a syntax-guided multi-task learning approach TurduckenGen. Specifically, we first explicitly append the type information to the code tokens to capture the representation of syntactic constraints. Then we formalize code generation with syntactic constraint representation as an auxiliary task to enable the model to learn the syntactic constraints of the code. Finally, the syntactically correct code is selected accurately from the multiple candidates with the help of the compiler feedback. Extensive experiments and comprehensive analysis demonstrate the effectiveness and general applicability of our approach after being compared with six state-of-the-art baselines on two Turducken-style code datasets. Finally, we conducted a human study and found the code quality generated by our approach is better than baselines in terms of code readability and semantic similarity.

cs.SE↗
arXiv2023

Is attention all you need in medical image analysis? A review

Medical imaging is a key component in clinical diagnosis, treatment planning and clinical trial design, accounting for almost 90% of all healthcare data. CNNs achieved performance gains in medical image analysis (MIA) over the last years. CNNs can efficiently model local pixel interactions and be trained on small-scale MI data. The main disadvantage of typical CNN models is that they ignore global pixel relationships within images, which limits their generalisation ability to understand out-of-distribution data with different 'global' information. The recent progress of Artificial Intelligence gave rise to Transformers, which can learn global relationships from data. However, full Transformer models need to be trained on large-scale data and involve tremendous computational complexity. Attention and Transformer compartments (Transf/Attention) which can well maintain properties for modelling global relationships, have been proposed as lighter alternatives of full Transformers. Recently, there is an increasing trend to co-pollinate complementary local-global properties from CNN and Transf/Attention architectures, which led to a new era of hybrid models. The past years have witnessed substantial growth in hybrid CNN-Transf/Attention models across diverse MIA problems. In this systematic review, we survey existing hybrid CNN-Transf/Attention models, review and unravel key architectural designs, analyse breakthroughs, and evaluate current and future opportunities as well as challenges. We also introduced a comprehensive analysis framework on generalisation opportunities of scientific and clinical impact, based on which new data-driven domain generalisation and adaptation methods can be stimulated.

cs.CV↗
arXiv2023

Spectroscopic Confirmation of CEERS NIRCam-selected Galaxies at $\boldsymbol{z \simeq 8-10}$

We present JWST/NIRSpec prism spectroscopy of seven galaxies selected from the Cosmic Evolution Early Release Science Survey (CEERS) NIRCam imaging with photometric redshifts z_phot>8. We measure emission line redshifts of z=7.65 and 8.64 for two galaxies, and z=9.77(+0.37,-0.29) and 10.01(+0.14,-0.19) for two others via the detection of continuum breaks consistent with Lyman-alpha opacity from a mostly neutral intergalactic medium. The presence (absense) of strong breaks (strong emission lines) give high confidence that these two galaxies are at z>9.6, but the break-derived redshifts have large uncertainties given the low spectral resolution and relatively low signal-to-noise of the CEERS NIRSpec prism data. The two z~10 sources are relatively luminous (M_UV<-20), with blue continua (-2.3 8 candidates with CEERS NIRSpec spectroscopy do not have secure redshifts, but the absence of emission lines in their spectra is consistent with redshifts z>9.6. We find that z>8 photometric redshifts are generally in agreement (within uncertainties) with the spectroscopic values. However, the photometric redshifts tend to be slightly overestimated (average Delta(z)=0.50+/-0.12), suggesting that current templates do not fully describe the spectra of very high-z sources. Overall, our results solidifies photometric evidence for a high space density of bright galaxies at z>8 compared to theoretical model predictions, and further disfavors an accelerated decline in the integrated UV luminosity density at z>8.

astro-ph.GA↗
arXiv2023

Multi-site, Multi-domain Airway Tree Modeling (ATM'22): A Public Benchmark for Pulmonary Airway Segmentation

Open international challenges are becoming the de facto standard for assessing computer vision and image analysis algorithms. In recent years, new methods have extended the reach of pulmonary airway segmentation that is closer to the limit of image resolution. Since EXACT'09 pulmonary airway segmentation, limited effort has been directed to quantitative comparison of newly emerged algorithms driven by the maturity of deep learning based approaches and clinical drive for resolving finer details of distal airways for early intervention of pulmonary diseases. Thus far, public annotated datasets are extremely limited, hindering the development of data-driven methods and detailed performance evaluation of new algorithms. To provide a benchmark for the medical imaging community, we organized the Multi-site, Multi-domain Airway Tree Modeling (ATM'22), which was held as an official challenge event during the MICCAI 2022 conference. ATM'22 provides large-scale CT scans with detailed pulmonary airway annotation, including 500 CT scans (300 for training, 50 for validation, and 150 for testing). The dataset was collected from different sites and it further included a portion of noisy COVID-19 CTs with ground-glass opacity and consolidation. Twenty-three teams participated in the entire phase of the challenge and the algorithms for the top ten teams are reviewed in this paper. Quantitative and qualitative results revealed that deep learning models embedded with the topological continuity enhancement achieved superior performance in general. ATM'22 challenge holds as an open-call design, the training data and the gold standard evaluation are available upon successful registration via its homepage.

eess.IV↗
arXiv2023

CDiffMR: Can We Replace the Gaussian Noise with K-Space Undersampling for Fast MRI?

Deep learning has shown the capability to substantially accelerate MRI reconstruction while acquiring fewer measurements. Recently, diffusion models have gained burgeoning interests as a novel group of deep learning-based generative methods. These methods seek to sample data points that belong to a target distribution from a Gaussian distribution, which has been successfully extended to MRI reconstruction. In this work, we proposed a Cold Diffusion-based MRI reconstruction method called CDiffMR. Different from conventional diffusion models, the degradation operation of our CDiffMR is based on \textit{k}-space undersampling instead of adding Gaussian noise, and the restoration network is trained to harness a de-aliaseing function. We also design starting point and data consistency conditioning strategies to guide and accelerate the reverse process. More intriguingly, the pre-trained CDiffMR model can be reused for reconstruction tasks with different undersampling rates. We demonstrated, through extensive numerical and visual experiments, that the proposed CDiffMR can achieve comparable or even superior reconstruction results than state-of-the-art models. Compared to the diffusion model-based counterpart, CDiffMR reaches readily competing results using only $1.6 \sim 3.4\%$ for inference time. The code is publicly available at https://github.com/ayanglab/CDiffMR.

eess.IV↗
arXiv2023

Observation of first-order quantum phase transitions and ferromagnetism in twisted double bilayer graphene

Twisted graphene multilayers are highly tunable flatband systems for developing new phases of matter. Thus far, while orbital ferromagnetism has been observed in valley polarized phases, the long-range orders of other correlated phases as well as the quantum phase transitions between different orders mostly remain unknown. Here, we report an observation of Coulomb interaction driven first-order quantum phase transitions and ferromagnetism in twisted double bilayer graphene (TDBG). At zero magnetic field, the transitions are revealed in a series of step-like abrupt resistance jumps with prominent hysteresis loop when either the displacement field (D) or the carrier density (n) is tuned across symmetry-breaking boundary near half filling, indicating a formation of ordered domains. It is worth noting that the good turnability and switching of these states gives a rise to a memory performance with a large on/off ratio. Moreover, when both spin and valley play the roles at finite magnetic field, we observe abundant first-order quantum phase transitions among normal metallic states from charge neutral point, orbital ferromagnetic states from quarter filling, and spin-polarized states from half filling. We interpret these first-order phase transitions in the picture of phase separations and spin domain percolations driven by multi-field tunable Coulomb interactions, in agreement with Lifshitz transition from Hartree-Fock calculations. The observed multi-filed tunable domain structure and its hysteresis resembles the characteristics of multiferroics, revealing intriguing magnetoelectric properties. Our result enriches the correlated phase diagram in TDBG for discovering novel exotic phases and quantum phase transitions, and it would benefit other twisted moiré systems as well.

cond-mat.mes-hall↗
arXiv2023

The Beauty or the Beast: Which Aspect of Synthetic Medical Images Deserves Our Focus?

Training medical AI algorithms requires large volumes of accurately labeled datasets, which are difficult to obtain in the real world. Synthetic images generated from deep generative models can help alleviate the data scarcity problem, but their effectiveness relies on their fidelity to real-world images. Typically, researchers select synthesis models based on image quality measurements, prioritizing synthetic images that appear realistic. However, our empirical analysis shows that high-fidelity and visually appealing synthetic images are not necessarily superior. In fact, we present a case where low-fidelity synthetic images outperformed their high-fidelity counterparts in downstream tasks. Our findings highlight the importance of comprehensive analysis before incorporating synthetic data into real-world applications. We hope our results will raise awareness among the research community of the value of low-fidelity synthetic images in medical AI algorithm training.

eess.IV↗
arXiv2023

NGDEEP Epoch 1: The Faint-End of the Luminosity Function at $z \sim$ 9-12 from Ultra-Deep JWST Imaging

We present a robust sample of very high-redshift galaxy candidates from the first epoch of {\it JWST}/NIRCam imaging from the Next Generation Extragalactic Exploratory Deep (NGDEEP) Survey. The NGDEEP NIRCam imaging in the Hubble Ultra Deep Field Parallel Field 2 (HUDF-Par2) reaches $m=30.4$ (5$σ$, point-source) in F277W, making it the deepest public {\it JWST} GO imaging dataset to date. We describe our detailed data reduction process of the six-filter broad-band {\it JWST}/NIRCam imaging, incorporating custom corrections for systematic effects to produce high-quality calibrated images. Using robust photometric redshift selection criteria, we identify a sample of 38 $z \gtrsim 9$ galaxy candidates. These objects span a redshift range of $z=8.5-15.8$, and apparent magnitudes of $m_\mathrm{F277W} = 27-30.5$ AB mag, reaching $\sim 1.5$ mag deeper than previous public {\it JWST} imaging surveys. We calculate the rest-frame ultraviolet (UV) luminosity function at $z \sim$ 9 and 11, and present a new measurement of the luminosity function faint-end slope at $z \sim 11$. There is no significant evolution in the faint-end slope and number density from $z=9$ to 11. Comparing our results with theoretical predictions, we find that some models produce better agreement at the faint end than the bright end. These results will help to constrain how stellar feedback impacts star formation at these early epochs.

astro-ph.GA↗
arXiv2023

VO2 Phase Change Electrodes in Li-ion Batteries

Use of electrode materials that show phase change behavior and hence drastic changes in electrochemical activity during operation, have not been explored for Li-ion batteries. Here we demonstrate the vanadium oxide (VO2) cathode that undergoes metal-insulator transition due to first-order structural phase transition at accessible temperature of 68°C for battery operation. Using a suitable electrolyte operable across the phase transition range and compatible with vanadium oxide cathodes, we studied the effect of electrode structure change on lithium insertion followed by the electrochemical characteristics above and below the phase transition temperature. The high-temperature VO2 phase shows significantly improved capacitance, enhanced current rate capabilities, improved electrical conductivity and lithium-ion diffusivity compared to the insulating low temperature phase. This opens up new avenues for electrode designs, allowing manipulation of electrochemical reactions around phase transition temperatures, and in particular enhancing electrochemical properties at elevated temperatures contrary to existing classes of battery chemistries that lead to performance deterioration at elevated temperatures.

physics.chem-ph↗
arXiv2023

You Don't Have to Be Perfect to Be Amazing: Unveil the Utility of Synthetic Images

Synthetic images generated from deep generative models have the potential to address data scarcity and data privacy issues. The selection of synthesis models is mostly based on image quality measurements, and most researchers favor synthetic images that produce realistic images, i.e., images with good fidelity scores, such as low Fréchet Inception Distance (FID) and high Peak Signal-To-Noise Ratio (PSNR). However, the quality of synthetic images is not limited to fidelity, and a wide spectrum of metrics should be evaluated to comprehensively measure the quality of synthetic images. In addition, quality metrics are not truthful predictors of the utility of synthetic images, and the relations between these evaluation metrics are not yet clear. In this work, we have established a comprehensive set of evaluators for synthetic images, including fidelity, variety, privacy, and utility. By analyzing more than 100k chest X-ray images and their synthetic copies, we have demonstrated that there is an inevitable trade-off between synthetic image fidelity, variety, and privacy. In addition, we have empirically demonstrated that the utility score does not require images with both high fidelity and high variety. For intra- and cross-task data augmentation, mode-collapsed images and low-fidelity images can still demonstrate high utility. Finally, our experiments have also showed that it is possible to produce images with both high utility and privacy, which can provide a strong rationale for the use of deep generative models in privacy-preserving applications. Our study can shore up comprehensive guidance for the evaluation of synthetic images and elicit further developments for utility-aware deep generative models in medical image synthesis.

cs.CV↗