arXiv subjects

Yao Liu

Publications and source records attributed to Yao Liu.

At least 91 records · Page 5Linked to original sources

arXiv2025

Formation of the dark-matter deficient S0 galaxy NGC 4111 under the tidal interactions

We present the high-sensitivity and large-scale atomic hydrogen (HI) observations towards lenticular (S0) galaxy NGC 4111 using the Five-hundred-meter Aperture Spherical Radio Telescope (FAST). The column density map shows that NGC4111 and seven other different types of galaxies share a huge HI gas complex. The data also suggest that NGC 4111 is interacting with seven galaxies. Moreover, we identified a rotating gas disk associated with NGC 4111 from the HI complex. Still, the HI disk rotation direction has deviated from its stellar disk about 34.2$^{\circ}$, indicating that the NGC 4111 galaxy is undergoing a transition from a spiral galaxy to an S0 galaxy by the tidal interactions. The obtained dark matter-to-stellar mass ratio of NGC4111 is 3.1$\pm$0.7, which is lower than the average value of the Local Universe, implying that the interactions may strip its dark matter. Our results suggest that in a galaxy group environment, tidal interactions have a significant effect on galaxy features.

astro-ph.GA↗

arXiv2025

Huge Stress-induced Adiabatic Temperature Change in a High-Toughness All-d-metal Heusler Alloy

The elastocaloric effect (eCE), referring to the thermal effect triggered by a uniaxial stress, provides a promising and versatile routine for green and high efficient thermal management. However, current eCE materials generally suffer from relatively low eCE and poor mechanical properties, hindering their practical applications. Here, we report a exceptionally huge eCE with a directly measured adiabatic temperature change of up to 57.2 K in a dual-phase all-d-metal Heusler Mn50Ni37.5Ti12.5 polycrystalline alloy, revealing an extra contribution to the latent heat during the stress-induced martensitic transformation from B2 to L10, and breaking the record of adiabatic temperature change for elastocaloric alloys. Moreover, thanks to the combined strengthening effect of d-d hybridization and well-dispersed secondary cubic γ phase, the alloy can endure a uniaxial stress up to 1760 MPa. Such an abnormal huge eCE is attributed to the combination of the enhanced entropy change associated with a stress-induced B2 to L10 martensitic transformation under higher stress, in contrast with the thermally induced B2 to 5-layer modulated structure one, and the high transformation fraction due to the multi-point nucleation facilitated by the γ phase dispersed in the main phase. This work provides insight into making full use of the transformation heat to enhance the caloric effect for high-efficient thermal management systems.

cond-mat.mtrl-sci↗

arXiv2025

Multi-Order Hyperbolic Graph Convolution and Aggregated Attention for Social Event Detection

Social event detection (SED) is a task focused on identifying specific real-world events and has broad applications across various domains. It is integral to many mobile applications with social features, including major platforms like Twitter, Weibo, and Facebook. By enabling the analysis of social events, SED provides valuable insights for businesses to understand consumer preferences and supports public services in handling emergencies and disaster management. Due to the hierarchical structure of event detection data, traditional approaches in Euclidean space often fall short in capturing the complexity of such relationships. While existing methods in both Euclidean and hyperbolic spaces have shown promising results, they tend to overlook multi-order relationships between events. To address these limitations, this paper introduces a novel framework, Multi-Order Hyperbolic Graph Convolution with Aggregated Attention (MOHGCAA), designed to enhance the performance of SED. Experimental results demonstrate significant improvements under both supervised and unsupervised settings. To further validate the effectiveness and robustness of the proposed framework, we conducted extensive evaluations across multiple datasets, confirming its superiority in tackling common challenges in social event detection.

cs.SI↗

arXiv2025

Bridging the Training-Inference Gap in LLMs by Leveraging Self-Generated Tokens

Language models are often trained to maximize the likelihood of the next token given past tokens in the training dataset. However, during inference time, they are utilized differently, generating text sequentially and auto-regressively by using previously generated tokens as input to predict the next one. Marginal differences in predictions at each step can cascade over successive steps, resulting in different distributions from what the models were trained for and potentially leading to unpredictable behavior. This paper proposes two simple approaches based on model own generation to address this discrepancy between the training and inference time. Our first approach is Batch-Scheduled Sampling, where, during training, we stochastically choose between the ground-truth token from the dataset and the model's own generated token as input to predict the next token. This is done in an offline manner, modifying the context window by interleaving ground-truth tokens with those generated by the model. Our second approach is Reference-Answer-based Correction, where we explicitly incorporate a self-correction capability into the model during training. This enables the model to effectively self-correct the gaps between the generated sequences and the ground truth data without relying on an external oracle model. By incorporating our proposed strategies during training, we have observed an overall improvement in performance compared to baseline methods, as demonstrated by our extensive experiments using summarization, general question-answering, and math question-answering tasks.

cs.LG↗

arXiv2025

Dust processing in the terrestrial planet-forming region of the PDS 70 disk

Dust grains in protoplanetary disks are the building blocks of planets. Investigating the dust composition and size, and their variation over time, is crucial for understanding the planet formation process. The PDS 70 disk is so far the only protoplanetary disk with concrete evidence for the presence of young planets. Mid-infrared spectra were obtained for PDS 70 by the Infrared Spectrograph (IRS) on the Spitzer Space Telescope (SST) and the Mid-Infrared Instrument (MIRI) on the James Webb Space Telescope (JWST) in 2007 and 2022, respectively. In this work, we investigate the dust mineralogy through a detailed decomposition of the observed mid-infrared spectra. The results show that both the dust size and crystallinity increased by a factor of about two during the two epochs of observation, indicating evident dust processing in the terrestrial planet-forming region of the PDS 70 disk. The dust size (~0.8 micron) and crystallinity (~6%) in the PDS 70 disk are similar to those of other disks, which implies that the two nascent planets, PDS 70b and PDS 70c located at radial distances of ~22AU and ~34AU, do not have a significant impact on the dust processing in the inner disk. The flux densities at wavelengths longer than ~16 micron measured by JWST/MIRI are only ~60% of those obtained by Spitzer/IRS. Based on self-consistent radiative transfer modeling, we found that such a strong variability in mid-infrared fluxes can be produced by adjustments to the dust density distribution and structure of the inner disk probably induced by planet-disk interaction.

astro-ph.EP↗

arXiv2025

LAMOST Reveals Long-lived Protoplanetary Disks

While both observations and theories demonstrate that protoplanetary disks are not expected to live much longer than $\sim$10 Myr, several examples of prolonged disks have been observed in the past. In this work, we perform a systematic search for aged YSOs still surrounded by protoplanetary disks in the M star catalog from the LAMOST archive. We identify 14 sources older than 10 Myr, still surrounded by protoplanetary disks and with ongoing accretion activities, significantly improving the census of the category known as the Peter Pan disks. The stellar parameters, variability and accretion properties of these objects, as well as their spatial distribution, are investigated. Nearly all of these objects are distributed far away from nearby associations and star forming regions, but show evidence of being members of open clusters. Investigating the correlation between mass accretion rates and stellar masses, we find these long-lived disks accrete at systematically lower levels, compared to their younger counterparts with similar stellar masses. Studying the evolution of mass accretion rates with stellar ages, we find these aged disks follow similar trend as young ones.

astro-ph.GA↗

arXiv2024

A note on Lévy-driven McKean-Vlasov SDEs under monotonicity

In this note, under a weak monotonicity and a weak coercivity, we address strong well-posedness of McKean-Vlasov stochastic differential equations (SDEs) driven by Lévy jump processes, where the coefficients are Lipschitz continuous (with respect to the measure variable) under the $L^β$-Wasserstein distance for $β\in[1,2].$ Moreover, the issue on the weak propagation of chaos (i.e., convergence in distribution via the convergence of the empirical measure) and the strong propagation of chaos (i.e., at the level paths by coupling) is explored simultaneously. To treat the strong well-posedness of McKean-Vlasov SDEs we are interested in, we investigate strong well-posedness of classical time-inhomogeneous SDEs with jumps under a local weak monotonicity and a global weak coercivity. Such a result is of independent interest, and, most importantly, can provide an available reference on strong well-posedness of Lévy-driven SDEs under the monotone condition, which nevertheless is missing for a long time. Based on the theory derived, along with the interlacing technique and the Banach fixed point theorem, the strong well-posedness of McKean-Vlasov SDEs driven by Lévy jump processes can be established. Additionally, as a potential extension, strong well-posedness and conditional propagation of chaos are treated for Lévy-driven McKean-Vlasov SDEs with common noise under a weak monotonicity.

math.PR↗

arXiv2024

Regularized Multi-LLMs Collaboration for Enhanced Score-based Causal Discovery

As the significance of understanding the cause-and-effect relationships among variables increases in the development of modern systems and algorithms, learning causality from observational data has become a preferred and efficient approach over conducting randomized control trials. However, purely observational data could be insufficient to reconstruct the true causal graph. Consequently, many researchers tried to utilise some form of prior knowledge to improve causal discovery process. In this context, the impressive capabilities of large language models (LLMs) have emerged as a promising alternative to the costly acquisition of prior expert knowledge. In this work, we further explore the potential of using LLMs to enhance causal discovery approaches, particularly focusing on score-based methods, and we propose a general framework to utilise the capacity of not only one but multiple LLMs to augment the discovery process.

cs.LG↗

arXiv2024

UPdec-Webb: A Dataset for Coaddition of JWST NIRCam Images

We present the application of the image coaddition algorithm, Up-sampling and PSF Deconvolution Coaddition (UPDC), for stacking multiple exposure images captured by the James Webb Space Telescope (JWST) Near-Infrared Camera (NIRCam). By addressing the point spread function (PSF) effect, UPDC provides visually enhanced and sharper images. Furthermore, the anti-aliasing and super-resolution capabilities of UPDC make it easier to deblend sources overlapped on images, yielding a higher accuracy of aperture photometry. We apply this algorithm to the SMACS J0723 imaging data. Comparative analysis with the Drizzle algorithm demonstrates significant improvements in detecting faint sources, achieving accurate photometry, and effectively deblending (super-resolution) closely packed sources. {As a result, we have newly detected a pair of close binary stars that were previously unresolvable in the original exposures or the Drizzled image.} These improvements significantly benefit various scientific projects conducted by JWST. The resulting dataset, named "UPdec-Webb", can be accessible through the official website of the Chinese Virtual Observatory (ChinaVO).

astro-ph.IM↗

arXiv2024

Dust mass in protoplanetary disks with porous dust opacities

ALMA surveys have suggested that protoplanetary disks are not massive enough to form the known exoplanet population, under the assumption that the millimeter continuum emission is optically thin. In this work, we investigate how the mass determination is influenced when the porosity of dust grains is considered in radiative transfer models. The results show that disks with porous dust opacities yield similar dust temperature, but systematically lower millimeter fluxes compared to disks incorporating compact dust grains. Moreover, we recalibrate the relation between dust temperature and stellar luminosity for a wide range of stellar parameters, and calculate the dust masses of a large sample of disks using the traditionally analytic approach. The median dust mass from our calculation is about 6 times higher than the literature result, and this is mostly driven by the different opacities of porous and compact grains. A comparison of the cumulative distribution function between disk dust masses and exoplanet masses show that the median exoplanet mass is about 2 times lower than the median dust mass, if grains are porous, and there are no exoplanetary systems with masses higher than the most massive disks. Our analysis suggests that adopting porous dust opacities may alleviate the mass budget problem for planet formation. As an example illustrating the combined effects of optical depth and porous dust opacities on the mass estimation, we conduct new IRAM/NIKA-2 observations toward the IRAS 04370+2559 disk and perform a detailed radiative transfer modeling of the spectral energy distribution. The best-fit dust mass is roughly 100 times higher than the value from the traditionally analytic calculation. Future spatially resolved observations at various wavelengths are required to better constrain the dust mass.

astro-ph.EP↗

arXiv2024

Deep HI Mapping of M 106 Group with FAST

We used FAST to conduct deep HI imaging of the entire M 106 group region, and have discovered a few new HI filaments and clouds. Three HI clouds/filaments are found in a region connecting DDO 120 and NGC 4288, indicating an interaction between these two galaxies. The HI features in this region suggest that DDO 120 is probably the origin of the HI stream extending from the northern end of NGC 4288 to M 106. This structure is similar to the SMC-LMC stream, but much longer, about 190 kpc. Furthermore, based on the distance measurements, we have determined the satellite galaxy members of M 106. With an absolute magnitude cutoff of M_B=-10, we obtained a sample of 11 member satellite galaxies for M 106. Using the observed HI mass with FAST, we studied the properties of satellite galaxies in M 106 and found that satellite galaxies with lower stellar masses exhibit more significant deviations from the star-forming main sequence (SFMS) in their specific star formation rates. Furthermore, the relationship between the HI mass of satellite galaxies and optical diameter generally follows the field galaxies relation. We discuss the possible mechanisms leading to the quenching in the M 106 group based on the new data from FAST

astro-ph.GA↗

arXiv2024

Causality-Aware Transformer Networks for Robotic Navigation

Current research in Visual Navigation reveals opportunities for improvement. First, the direct adoption of RNNs and Transformers often overlooks the specific differences between Embodied AI and traditional sequential data modelling, potentially limiting its performance in Embodied AI tasks. Second, the reliance on task-specific configurations, such as pre-trained modules and dataset-specific logic, compromises the generalizability of these methods. We address these constraints by initially exploring the unique differences between Navigation tasks and other sequential data tasks through the lens of Causality, presenting a causal framework to elucidate the inadequacies of conventional sequential methods for Navigation. By leveraging this causal perspective, we propose Causality-Aware Transformer (CAT) Networks for Navigation, featuring a Causal Understanding Module to enhance the models's Environmental Understanding capability. Meanwhile, our method is devoid of task-specific inductive biases and can be trained in an End-to-End manner, which enhances the method's generalizability across various contexts. Empirical evaluations demonstrate that our methodology consistently surpasses benchmark performances across a spectrum of settings, tasks and simulation environments. Extensive ablation studies reveal that the performance gains can be attributed to the Causal Understanding Module, which demonstrates effectiveness and efficiency in both Reinforcement Learning and Supervised Learning settings.

cs.RO↗

arXiv2024

Uncertainties of the dust grain size in protoplanetary disks retrieved from millimeter continuum observations

Investigating the dust grain size and its dependence on substructures in protoplanetary disks is a crucial step in understanding the initial process of planet formation. Spectral indices derived from millimeter observations are used as a common probe for grain size. Converting observed spectral indices into grain sizes is a complex task that involves solving the radiative transfer equation, taking into account the disk structure and dust properties. In this work, we ran reference radiative transfer models with known disk properties, and generated four synthetic images at wavelengths of 0.8, 1.3, 3, and 7.8 mm, representing high-resolution continuum observations. Rings and gaps were considered in the setup. We fit the synthetic images using the analytic solution of the radiative transfer equation to investigate the circumstances under which the input grain sizes can be recovered. The results show that fitting images at only two wavelengths is not sufficient to retrieve the grain size. Fitting three images improves the retrieval of grain size, but the dust surface density is still not well recovered. When taking all of the four images into account, degeneracies between different parameters are highly reduced, and consequently the best-fit grain sizes are consistent with the reference setup at almost all radii. We find that the inclination angle has a significant impact on the fitting results. For disks with low inclinations, the analytic approach works quite well. However, when the disk is tilted above about 60 degree, neither the grain size nor the dust surface density can be constrained, as the inclination effect will smooth out all substructures in the radial intensity profile of the disk.

astro-ph.EP↗

arXiv2024

Cognitive Bias in Decision-Making with LLMs

Large language models (LLMs) offer significant potential as tools to support an expanding range of decision-making tasks. Given their training on human (created) data, LLMs have been shown to inherit societal biases against protected groups, as well as be subject to bias functionally resembling cognitive bias. Human-like bias can impede fair and explainable decisions made with LLM assistance. Our work introduces BiasBuster, a framework designed to uncover, evaluate, and mitigate cognitive bias in LLMs, particularly in high-stakes decision-making tasks. Inspired by prior research in psychology and cognitive science, we develop a dataset containing 13,465 prompts to evaluate LLM decisions on different cognitive biases (e.g., prompt-induced, sequential, inherent). We test various bias mitigation strategies, while proposing a novel method utilizing LLMs to debias their own human-like cognitive bias within prompts. Our analysis provides a comprehensive picture of the presence and effects of cognitive bias across commercial and open-source models. We demonstrate that our selfhelp debiasing effectively mitigates model answers that display patterns akin to human cognitive bias without having to manually craft examples for each bias.

cs.AI↗

arXiv2024

Learning the Target Network in Function Space

We focus on the task of learning the value function in the reinforcement learning (RL) setting. This task is often solved by updating a pair of online and target networks while ensuring that the parameters of these two networks are equivalent. We propose Lookahead-Replicate (LR), a new value-function approximation algorithm that is agnostic to this parameter-space equivalence. Instead, the LR algorithm is designed to maintain an equivalence between the two networks in the function space. This value-based equivalence is obtained by employing a new target-network update. We show that LR leads to a convergent behavior in learning the value function. We also present empirical results demonstrating that LR-based target-network updates significantly improve deep RL on the Atari benchmark.

cs.LG↗

arXiv2024

EXTRACT: Efficient Policy Learning by Extracting Transferable Robot Skills from Offline Data

Most reinforcement learning (RL) methods focus on learning optimal policies over low-level action spaces. While these methods can perform well in their training environments, they lack the flexibility to transfer to new tasks. Instead, RL agents that can act over useful, temporally extended skills rather than low-level actions can learn new tasks more easily. Prior work in skill-based RL either requires expert supervision to define useful skills, which is hard to scale, or learns a skill-space from offline data with heuristics that limit the adaptability of the skills, making them difficult to transfer during downstream RL. Our approach, EXTRACT, instead utilizes pre-trained vision language models to extract a discrete set of semantically meaningful skills from offline data, each of which is parameterized by continuous arguments, without human supervision. This skill parameterization allows robots to learn new tasks by only needing to learn when to select a specific skill and how to modify its arguments for the specific task. We demonstrate through experiments in sparse-reward, image-based, robot manipulation environments that EXTRACT can more quickly learn new tasks than prior works, with major gains in sample efficiency and performance over prior skill-based RL. Website at https://www.jessezhang.net/projects/extract/.

cs.RO↗

arXiv2024

Privacy-Preserving Multimedia Mobile Cloud Computing Using Protective Perturbation

Mobile cloud computing has been adopted in many multimedia applications, where the resource-constrained mobile device sends multimedia data (e.g., images) to remote cloud servers to request computation-intensive multimedia services (e.g., image recognition). While significantly improving the performance of the mobile applications, the cloud-based mechanism often causes privacy concerns as the multimedia data and services are offloaded from the trusted user device to untrusted cloud servers. Several recent studies have proposed perturbation-based privacy preserving mechanisms, which obfuscate the offloaded multimedia data to eliminate privacy exposures without affecting the functionality of the remote multimedia services. However, the existing privacy protection approaches require the deployment of computation-intensive perturbation generation on the resource-constrained mobile devices. Also, the obfuscated images are typically not compliant with the standard image compression algorithms and suffer from significant bandwidth consumption. In this paper, we develop a novel privacy-preserving multimedia mobile cloud computing framework, namely $PMC^2$, to address the resource and bandwidth challenges. $PMC^2$ employs secure confidential computing in the cloud to deploy the perturbation generator, which addresses the resource challenge while maintaining the privacy. Furthermore, we develop a neural compressor specifically trained to compress the perturbed images in order to address the bandwidth challenge. We implement $PMC^2$ in an end-to-end mobile cloud computing system, based on which our evaluations demonstrate superior latency, power efficiency, and bandwidth consumption achieved by $PMC^2$ while maintaining high accuracy in the target multimedia service.

cs.MM↗

arXiv2024

Budgeting Counterfactual for Offline RL

The main challenge of offline reinforcement learning, where data is limited, arises from a sequence of counterfactual reasoning dilemmas within the realm of potential actions: What if we were to choose a different course of action? These circumstances frequently give rise to extrapolation errors, which tend to accumulate exponentially with the problem horizon. Hence, it becomes crucial to acknowledge that not all decision steps are equally important to the final outcome, and to budget the number of counterfactual decisions a policy make in order to control the extrapolation. Contrary to existing approaches that use regularization on either the policy or value function, we propose an approach to explicitly bound the amount of out-of-distribution actions during training. Specifically, our method utilizes dynamic programming to decide where to extrapolate and where not to, with an upper bound on the decisions different from behavior policy. It balances between the potential for improvement from taking out-of-distribution actions and the risk of making errors due to extrapolation. Theoretically, we justify our method by the constrained optimality of the fixed point solution to our $Q$ updating rules. Empirically, we show that the overall performance of our method is better than the state-of-the-art offline RL methods on tasks in the widely-used D4RL benchmarks.

cs.LG↗