arXiv subjects

Shu Wang

Publications and source records attributed to Shu Wang.

At least 127 records · Page 7Linked to original sources

arXiv2024

Fast and label-free 3D virtual H&E histology via active modulation-assisted dynamic full-field OCT

Pathological features are the gold standard for tumor diagnosis, guiding treatment and prognosis. However, standard histopathological process is labor-intensive and time-consuming, while frozen sections have lower accuracy. Dynamic full-field optical coherence tomography (D-FFOCT) offers rapid histologic information by measuring the subcellular dynamics of fresh, unprocessed tissues. However, D-FFOCT images suffer from abrupt shifts in hue and brightness, which is confusing for pathologists and diminish their interpretability and reliability. Here, we present active phase modulation-assisted D-FFOCT (APMD-FFOCT) to improve the imaging stability and enhance the contrast of static tissues. This enables us to further employ an unsupervised deep learning to convert APMD-FFOCT images into virtual hematoxylin and eosin (H&E) stained images for the first time. Three-dimensional (3D) virtual H&E-stained images have been obtained at a scanning rate of 1 frame per second, as demonstrated in cancer diagnosis for human central nervous system and breast. The results prove that this new method will play a unique and important role in intraoperative histology.

physics.med-ph↗

arXiv2024

Ag2Manip: Learning Novel Manipulation Skills with Agent-Agnostic Visual and Action Representations

Autonomous robotic systems capable of learning novel manipulation tasks are poised to transform industries from manufacturing to service automation. However, modern methods (e.g., VIP and R3M) still face significant hurdles, notably the domain gap among robotic embodiments and the sparsity of successful task executions within specific action spaces, resulting in misaligned and ambiguous task representations. We introduce Ag2Manip (Agent-Agnostic representations for Manipulation), a framework aimed at surmounting these challenges through two key innovations: a novel agent-agnostic visual representation derived from human manipulation videos, with the specifics of embodiments obscured to enhance generalizability; and an agent-agnostic action representation abstracting a robot's kinematics to a universal agent proxy, emphasizing crucial interactions between end-effector and object. Ag2Manip's empirical validation across simulated benchmarks like FrankaKitchen, ManiSkill, and PartManip shows a 325% increase in performance, achieved without domain-specific demonstrations. Ablation studies underline the essential contributions of the visual and action representations to this success. Extending our evaluations to the real world, Ag2Manip significantly improves imitation learning success rates from 50% to 77.5%, demonstrating its effectiveness and generalizability across both simulated and physical environments.

cs.RO↗

arXiv2024

Revisiting the dust torus size-luminosity relation based on a uniform reverberation mapping analysis

We investigate the torus size -- luminosity relation of Type 1 AGNs based on the reverberation-mapping analysis using the light curves of the optical continuum and the IR continuum obtained with the W1 and W2-bands of the Wide-field Infrared Survey Explorer (WISE) survey. The final sample consists of 446 and 416 AGNs, respectively, for W1 and W2-band light curves, covering a large dynamic range of bolometric luminosity from $10^{43.4}$ to $10^{47.6}$ $erg \, s^{-1}$, which show reliable lag measurements based on our quality assessment analysis. After correcting for the accretion disk contamination in the observed IR flux, we constrain the torus size ($R_{dust}$) and AGN bolometric luminosity ($L_{bol}$) relationship with the best-fit slope of 0.39 (0.33) for the W1- (W2-) band, which is shallower than expected from the dust radiation equilibrium model. By combining the previous K-band lag measurements, we find that the measured torus size depends on the observed wavelength of the dust radiation, as $R_{dust,K}:R_{dust,W1}:R_{dust,W2}$ = 1.0:1.5:1.8 ($R_{dust} \, \propto \, λ^{0.80}$) at $L_{bol}$ = $10^{46} \, erg \, s^{-1}$, confirming a stratified structure of the torus, where wavelength-dependent emissions originate from distinct regions of the torus. By investigating the deviation from the best-fit torus size -- luminosity relation, we find a moderate correlation between the offset from the $R_{dust}$--$L_{bol}$ relation and Eddington ratio. This suggests a possible influence of the Eddington ratio on the observed flattening of the $R_{dust}$--$L_{bol}$ relationship.

astro-ph.GA↗

arXiv2024

GeoGalactica: A Scientific Large Language Model in Geoscience

Large language models (LLMs) have achieved huge success for their general knowledge and ability to solve a wide spectrum of tasks in natural language processing (NLP). Due to their impressive abilities, LLMs have shed light on potential inter-discipline applications to foster scientific discoveries of a specific domain by using artificial intelligence (AI for science, AI4S). In the meantime, utilizing NLP techniques in geoscience research and practice is wide and convoluted, contributing from knowledge extraction and document classification to question answering and knowledge discovery. In this work, we take the initial step to leverage LLM for science, through a rather straightforward approach. We try to specialize an LLM into geoscience, by further pre-training the model with a vast amount of texts in geoscience, as well as supervised fine-tuning (SFT) the resulting model with our custom collected instruction tuning dataset. These efforts result in a model GeoGalactica consisting of 30 billion parameters. To our best knowledge, it is the largest language model for the geoscience domain. More specifically, GeoGalactica is from further pre-training of Galactica. We train GeoGalactica over a geoscience-related text corpus containing 65 billion tokens, preserving as the largest geoscience-specific text corpus. Then we fine-tune the model with 1 million pairs of instruction-tuning data consisting of questions that demand professional geoscience knowledge to answer. In this technical report, we will illustrate in detail all aspects of GeoGalactica, including data collection, data cleaning, base model selection, pre-training, SFT, and evaluation. We open-source our data curation tools and the checkpoints of GeoGalactica during the first 3/4 of pre-training.

cs.CL↗

arXiv2024

Identifying changing-look AGNs using variability characteristics

Changing-look (CL) Active Galactic Nuclei (AGNs), characterized by appearance/disappearance of broad emission lines in the span of a few years, present a challenge for the AGN unified model, whereby the Type 1 vs. Type 2 dichotomy results from orientation effects alone. We present a systematic study of a large sample of spectroscopically classified AGNs, using optical variability data from the Zwicky Transient Facility (ZTF) as well as follow-up spectroscopy data. We demonstrate that Type 1 vs. 2 AGN can be neatly separated on the basis of the variability metric $σ_{\rm QSO}$, which quantifies the resemblance of a light curve to a damp random walk model. For a small sub-sample, however, the ZTF light curves are inconsistent with their previous classification, suggesting the occurrence of a CL event. Specifically, we identify 35 (12) turn-on (turn-off) CL AGN candidates at $z < 0.35$. Based on follow-up spectroscopy, we confirm 17 (4) turn-on (turn-off) CL AGNs out of 21 (5) candidates, presenting a high success rate of our method. Our results suggest that the occurrence rate of CL AGNs is $\sim$0.3% over timescales of 5 to 20 years, and confirm that the CL transition typically occurs at the Eddington ratio of $\leq 0.01$.

astro-ph.GA↗

arXiv2024

The Optical to Infrared $0.6-5.3\,{\rm μm}$ Dust Extinction Law of the Milky Way with JWST NIRSpec: Westerlund 2

The interstellar extinction law is important for interpreting observations and inferring the properties of interstellar dust grains. Based on the 993 prism/CLEAR spectra from the James Webb Space Telescope (JWST), we investigate $0.6-5.3\,{\rm μm}$ interstellar dust extinction law. We propose a pair method to obtain the reddening curves based only on JWST observed spectra. Most of the high extinction sources are toward the young star cluster Westerlund 2. The infrared (IR) $1.0-5.3\,{\rm μm}$ reddening curves agree with the power-law $A_λ\propto λ^{-α}$ well. We determine an average value of $α=1.98\pm0.15$, which is consistent with the average value of the Galaxy. We find that $α$ may be variable and independent of $R_{\rm V}$. With the derived $α$, we convert the reddening curves into the extinction curves and establish the non-parameterized $α$-dependent extinction curves in the wavelength range of $0.6-5.3\,{\rm μm}$. At $λ<1\,{\rm μm}$, the derived extinction law is not well described by the parameterized power-law type curve. Our non-parameterized $α$-dependent extinction curves are suitable for the extinction correction of JWST-based photometry and spectra measurements at $0.6-5.3\,{\rm μm}$. We also provide the extinction coefficients for the JWST NIRCam bandpasses with different $α$.

astro-ph.GA↗

arXiv2024

Tracing the Galactic disk from the kinematics of Gaia Cepheids

Classical Cepheids (CCs) are excellent tracers for understanding the structure of the Milky Way disk. The latest Gaia Data Release 3 provides a large number of line-of-sight velocity information for Galactic CCs, offering an opportunity for studying the kinematics of the Milky Way. We determine the three-dimensional velocities of 2057 CCs relative to the Galactic center. From the projections of the 3D velocities onto the XY plane of the Galactic disk, we find that $V_{R}$ and $V_ϕ$ velocities of the northern and southern warp (directions with highest amplitude) are different. This phenomenon may be related to the warp precession or the asymmetry of the warp structure. By investigating the kinematic warp model, we find that the vertical velocity of CCs is more suitable for constraining the warp precession rate than the line of nodes angles. Our results suggest that CCs at $12-14$ kpc are the best sample for determining the Galactic warp precession rate. Based on the spatial structure parameters of Cepheid warp from Chen et al (arXiv:1902.00998), we determine a warp precession rate of $ω= 4.9\pm1.6$ km s$^{-1}$ kpc$^{-1}$ at 13 kpc, which supports a low precession rate in the warp model. In the future, more kinematic information on CCs will help to better constrain the structure and evolution of the Milky Way.

astro-ph.GA↗

arXiv2024

Vanishing viscosity limit to the planar rarefaction wave with vacuum for 3-D full compressible Navier-Stokes equations with temperature-dependent transport coefficients

In this paper, we construct a family of global-in-time solutions of the 3-D full compressible Navier-Stokes (N-S) equations with temperature-dependent transport coefficients (including viscosity and heat-conductivity), and show that at arbitrary times {and arbitrary strength} this family of solutions converges to planar rarefaction waves connected to the vacuum as the viscosity vanishes in the sense of $L^\infty(\R^3)$. We consider the Cauchy problem in $\R^3$ with perturbations of the infinite global norm, particularly, periodic perturbations. To deal with the infinite oscillation, we construct a suitable ansatz carrying this periodic oscillation such that the difference between the solution and the ansatz belongs to some Sobolev space and thus the energy method is feasible. The novelty of this paper is that the viscosity and heat-conductivity are temperature-dependent and degeneracies caused by vacuum. Thus the a priori assumptions and two Gagliardo-Nirenberg type inequalities are essentially used. Next, more careful energy estimates are carried out in this paper, by studying the zero and non-zero modes of the solutions, we obtain not only the convergence rate concerning the viscosity and heat conductivity coefficients but also the exponential time decay rate for the non-zero mode.

math.AP↗

arXiv2024

Evolved Massive Stars at Low-metallicity VII. the Lower Mass Limit of Red Supergiant Population in the Large Magellanic Cloud

The precise definition of the lower mass limit of red supergiant stars (RSGs) is an open question in astrophysics and does not attract too much attention. Here we assemble a spectroscopic evolved cool star sample with 6,602 targets, including RSGs, asymptotic giant branch stars, and red giant branch stars, in the Large Magellanic Cloud based on \textit{Gaia} DR3 and SDSS-IV/APOGEE-2. The reference spectrum of each stellar population is built according to the quantile range of relative intensity ($1\%\sim99\%$). Five different methods, e.g., chi-square ($χ^2$), cosine similarity (CS), machine learning (ML), equivalent width (EW), and line ratio (LR), are used in order to separate different stellar populations. The ML and $χ^2$ provide the best and relatively consistent prediction of certain population. The derived lower limit of the RSG population is able to reach to the $\rm K_S$-band tip of red giant branch ($\rm K_S~$$\approx12.0$ mag), indicating a luminosity as low as about $10^{3.5}~L_{\sun}$, which corresponds to a stellar radius only about $100~R_{\sun}$. Given the mass-luminosity relation of $L/L_\sun =f(M/M_\sun)^3$ with $f\approx15.5\pm3$ and taking into account of the mass loss of faint RSGs up to now, the minimal initial mass of the RSG population would be about $6.1\pm0.4~M_\sun$, which is much lower than the traditional threshold of $8~M_\sun$ for the massive stars. This is the first spectroscopic evidence, indicating that the lower mass limit of RSG population is around $6~M_\sun$. However, the destinies of such faint RSGs are still elusive and may have large impact on the stellar evolutionary and supernova models.

astro-ph.SR↗

arXiv2024

A Loop-Opening Model for the Intrinsic Fracture Energy of Polymer Networks

We present a loop-opening model that accounts for the molecular details of the intrinsic fracture energy for fracturing polymer networks. This model includes not only the energy released from the scission of bridging chains but also the subsequent energy released from the network continuum. Scission of a bridging chain releases the crosslinks and opens the corresponding topological loop. The released crosslinks will be caught by the opened loop to reach a new force-balanced state. The amount of energy released from the network continuum is limited by the stretchability of the opened loop. Based on this loop-opening process, we suggest that the intrinsics fracture energy per broken chain approximately scales with the product of the fracture force and the contour length of the opened loop. This model predicts an intrinsic fracture energy that aligns well with various experimental data on the fracture of polymer networks.

cond-mat.mtrl-sci↗

arXiv2024

A Universal Scaling Law for Intrinsic Fracture Energy of Networks

Networks of interconnected materials permeate throughout nature, biology, and technology due to exceptional mechanical performance. Despite the importance of failure resistance in network design and utility, no existing physical model effectively links strand mechanics and connectivity to predict bulk fracture. Here, we reveal a universal scaling law that bridges these levels to predict the intrinsic fracture energy of diverse networks. Simulations and experiments demonstrate its remarkable applicability to a breadth of strand constitutive behaviors, topologies, dimensionalities, and length scales. We show that local strand rupture and nonlocal energy release contribute synergistically to the measured intrinsic fracture energy in networks. These effects coordinate such that the intrinsic fracture energy scales independent of the energy to rupture a strand; it instead depends on the strand rupture force, breaking length, and connectivity. Our scaling law establishes a physical basis for understanding network fracture and a framework for fabricating tough materials from networks across multiple length scales.

cond-mat.mtrl-sci↗

arXiv2023

BinGo: Identifying Security Patches in Binary Code with Graph Representation Learning

A timely software update is vital to combat the increasing security vulnerabilities. However, some software vendors may secretly patch their vulnerabilities without creating CVE entries or even describing the security issue in their change log. Thus, it is critical to identify these hidden security patches and defeat potential N-day attacks. Researchers have employed various machine learning techniques to identify security patches in open-source software, leveraging the syntax and semantic features of the software changes and commit messages. However, all these solutions cannot be directly applied to the binary code, whose instructions and program flow may dramatically vary due to different compilation configurations. In this paper, we propose BinGo, a new security patch detection system for binary code. The main idea is to present the binary code as code property graphs to enable a comprehensive understanding of program flow and perform a language model over each basic block of binary code to catch the instruction semantics. BinGo consists of four phases, namely, patch data pre-processing, graph extraction, embedding generation, and graph representation learning. Due to the lack of an existing binary security patch dataset, we construct such a dataset by compiling the pre-patch and post-patch source code of the Linux kernel. Our experimental results show BinGo can achieve up to 80.77% accuracy in identifying security patches between two neighboring versions of binary code. Moreover, BinGo can effectively reduce the false positives and false negatives caused by the different compilers and optimization levels.

cs.CR↗

arXiv2023

Decay rate to the planar viscous shock wave for multi-dimensional scalar conservation laws

In this paper, we study the time-decay rate toward the planar viscous shock wave for multi-dimensional (m-d) scalar viscous conservation law. We first decompose the perturbation into zero and non-zero mode, and then introduce the anti-derivative of the zero mode. Though an $L^p$ estimate and the area inequality introduced in \cite{DHS2020}, we obtained the decay rate for planar shock wave for n-d scalar viscous conservation law for all $n\geq1$. The initial perturbations we studied are small, i.e., $\|Φ_0\|_{H^2}\bigcap\|Φ_0\|_{L^p}\le \varepsilon$, where $Φ_0$ is the anti-derivative of the zero mode of initial perturbation and $\varepsilon$ is a small constant, see \cref{antiderivative}. It is noted that there is no additional requirement on $Φ_0$, i.e., $Φ_0(x_1)$ only belongs to $H^2(\R)$. Thus, there are essential differences from previous results, in which the initial data is required to belong to some weighted Sobolev space, cf.\cite{Goo1989,KM1985}. Moreover, the exponential decay rate of the non-zero mode is also obtained.

math.AP↗

arXiv2023

The Seoul National University AGN Monitoring Project III: H$β$ lag measurements of 32 luminous AGNs and the high-luminosity end of the size--luminosity relation

We present the main results from a long-term reverberation mapping campaign carried out for the Seoul National University Active Galactic Nuclei (AGN) Monitoring Project. High-quality data were obtained during 2015-2021 for 32 luminous AGNs (i.e., continuum luminosity in the range of $10^{44-46}$ erg s$^{-1}$) at a regular cadence, of 20-30 days for spectroscopy and 3-5 days for photometry. We obtain time lag measurements between the variability in the H$β$ emission and the continuum for 32 AGNs; twenty-five of those have the best lag measurements based on our quality assessment, examining correlation strength, and the posterior lag distribution. Our study significantly increases the current sample of reverberation-mapped AGNs, particularly at the moderate to high luminosity end. Combining our results with literature measurements, we derive a H$β$ broad line region size--luminosity relation with a shallower slope than reported in the literature. For a given luminosity, most of our measured lags are shorter than the expectation, implying that single-epoch black hole mass estimators based on previous calibrations could suffer large systematic uncertainties.

astro-ph.GA↗

arXiv2023

Having Difficulty Understanding Manuals? Automatically Converting User Manuals into Instructional Videos

While users tend to perceive instructional videos as an experience rather than a lesson with a set of instructions, instructional videos are more effective and appealing than textual user manuals and eliminate the ambiguity in text-based descriptions. However, most software vendors only offer document manuals that describe how to install and use their software, leading burden for non-professionals to comprehend the instructions. In this paper, we present a framework called M2V to generate instructional videos automatically based on the provided instructions and images in user manuals. M2V is a two-step framework. First, an action sequence is extracted from the given user manual via natural language processing and computer vision techniques. Second, M2V operates the software sequentially based on the extracted actions; meanwhile, the operation procedure is recorded into an instructional video. We evaluate the usability of automatically generated instructional videos via user studies and an online survey. The evaluation results show, with our toolkit, the generated instructional videos can better assist non-professional end users with the software operations. Moreover, more than 85% of survey participants prefer to use the instructional videos rather than the original user manuals.

cs.HC↗

arXiv2023

A Multimodal Ecological Civilization Pattern Recommendation Method Based on Large Language Models and Knowledge Graph

The Ecological Civilization Pattern Recommendation System (ECPRS) aims to recommend suitable ecological civilization patterns for target regions, promoting sustainable development and reducing regional disparities. However, the current representative recommendation methods are not suitable for recommending ecological civilization patterns in a geographical context. There are two reasons for this. Firstly, regions have spatial heterogeneity, and the (ECPRS)needs to consider factors like climate, topography, vegetation, etc., to recommend civilization patterns adapted to specific ecological environments, ensuring the feasibility and practicality of the recommendations. Secondly, the abstract features of the ecological civilization patterns in the real world have not been fully utilized., resulting in poor richness in their embedding representations and consequently, lower performance of the recommendation system. Considering these limitations, we propose the ECPR-MML method. Initially, based on the novel method UGPIG, we construct a knowledge graph to extract regional representations incorporating spatial heterogeneity features. Following that, inspired by the significant progress made by Large Language Models (LLMs) in the field of Natural Language Processing (NLP), we employ Large LLMs to generate multimodal features for ecological civilization patterns in the form of text and images. We extract and integrate these multimodal features to obtain semantically rich representations of ecological civilization. Through extensive experiments, we validate the performance of our ECPR-MML model. Our results show that F1@5 is 2.11% higher compared to state-of-the-art models, 2.02% higher than NGCF, and 1.16% higher than UGPIG. Furthermore, multimodal data can indeed enhance recommendation performance. However, the data generated by LLM is not as effective as real data to a certain extent.

cs.IR↗

arXiv2023

Liver Tumor Screening and Diagnosis in CT with Pixel-Lesion-Patient Network

Liver tumor segmentation and classification are important tasks in computer aided diagnosis. We aim to address three problems: liver tumor screening and preliminary diagnosis in non-contrast computed tomography (CT), and differential diagnosis in dynamic contrast-enhanced CT. A novel framework named Pixel-Lesion-pAtient Network (PLAN) is proposed. It uses a mask transformer to jointly segment and classify each lesion with improved anchor queries and a foreground-enhanced sampling loss. It also has an image-wise classifier to effectively aggregate global information and predict patient-level diagnosis. A large-scale multi-phase dataset is collected containing 939 tumor patients and 810 normal subjects. 4010 tumor instances of eight types are extensively annotated. On the non-contrast tumor screening task, PLAN achieves 95% and 96% in patient-level sensitivity and specificity. On contrast-enhanced CT, our lesion-level detection precision, recall, and classification accuracy are 92%, 89%, and 86%, outperforming widely used CNN and transformers for lesion segmentation. We also conduct a reader study on a holdout set of 250 cases. PLAN is on par with a senior human radiologist, showing the clinical significance of our results.

eess.IV↗

arXiv2023

Hybrid Reinforcement Learning for Optimizing Pump Sustainability in Real-World Water Distribution Networks

This article addresses the pump-scheduling optimization problem to enhance real-time control of real-world water distribution networks (WDNs). Our primary objectives are to adhere to physical operational constraints while reducing energy consumption and operational costs. Traditional optimization techniques, such as evolution-based and genetic algorithms, often fall short due to their lack of convergence guarantees. Conversely, reinforcement learning (RL) stands out for its adaptability to uncertainties and reduced inference time, enabling real-time responsiveness. However, the effective implementation of RL is contingent on building accurate simulation models for WDNs, and prior applications have been limited by errors in simulation training data. These errors can potentially cause the RL agent to learn misleading patterns and actions and recommend suboptimal operational strategies. To overcome these challenges, we present an improved "hybrid RL" methodology. This method integrates the benefits of RL while anchoring it in historical data, which serves as a baseline to incrementally introduce optimal control recommendations. By leveraging operational data as a foundation for the agent's actions, we enhance the explainability of the agent's actions, foster more robust recommendations, and minimize error. Our findings demonstrate that the hybrid RL agent can significantly improve sustainability, operational efficiency, and dynamically adapt to emerging scenarios in real-world WDNs.

cs.AI↗