arXiv subjects

Haoran Yu

Publications and source records attributed to Haoran Yu.

53 records · Page 3Linked to original sources

arXiv2024

A new approach to constraining properties of AGN host galaxies by combining image and SED decomposition: testing upon the MBH-M* relation

The outshining light from active galactic nuclei (AGNs) poses significant challenges in studying the properties of AGN host galaxies. To address this issue, we propose a novel approach which combines image decomposition and spectral energy distribution (SED) decomposition to constrain properties of AGN host galaxies. Image decomposition allows us to disentangle optical flux into AGN and stellar components, thereby providing additional constraints on the SED models to derive more refined stellar mass. To test the viability of this approach, we obtained a sample of 24 X-ray selected type-I AGNs with redshifts ranging from 0.73 to 2.47. We estimated the stellar masses for our sample and found that our results are generally consistent with earlier estimates based on different methods. Through examining the posterior distribution of stellar masses, we find that our method could derive better constrained results compared to previous SED decomposition methods. With the derived stellar masses, we further studied the MBH-M* relation of our sample, finding a higher intrinsic scatter in the correlation for our entire sample compared to the local quiescent correlation, which could be caused by a few black hole monsters in our sample. We propose that based on our method, future works could extend to larger samples of high-redshift AGN host galaxies, thereby enhancing our understanding of their properties.

astro-ph.GA↗

arXiv2024

Multiwavelength Properties of Infrared-Faint Radio Sources Based on Spectral Energy Distribution Analysis

Infrared-faint radio sources (IFRSs) are believed to be a rare class of radio-loud active galactic nuclei (RL AGN) characterized by their high radio-to-infrared flux density ratios of up to several thousands. Previous studies have shown that a fraction of IFRSs are likely to be hosted in dust-obscured galaxies (DOGs). In this paper, our aim was to probe the dust properties, star formation rate (SFR), and AGN activity of IFRSs by modeling the UV-to-infrared spectral energy distribution (SED) of 20 IFRSs with spectroscopic redshifts ranging from 1.2 to 3.7. We compare the Bayesian evidence of a three-component model (stellar, AGN and cold dust) with that of a two-component model (stellar and cold dust) for six IFRSs in our sample with far-infrared (FIR) photometry and find that the three-component model has significantly higher Bayesian evidence, suggesting that IFRSs are most likely to be AGN. The median SED of our IFRS sample shows similarities to AGN-starburst composite in the IR regime. The derived IR luminosities of IFRSs indicate that they are low-luminosity counterparts of high-redshift radio galaxies. We disentangle the contributions of AGN-heated and star-formation-heated dust to the IR luminosity of IFRSs and find that our sample is likely AGN-dominated. However, despite the evidence for significant impact of AGN on the host galaxy, the AGN luminosity of our sample does not show correlation with the SFR of the sources.

astro-ph.GA↗

arXiv2024

Size Growth on Short Timescales of Star-Forming Galaxies: Insights from Size Variation with Rest-Frame Wavelength with JADES

We investigate size variation with rest-frame wavelength for star-forming galaxies based on the second JWST Advanced Deep Extragalactic Survey data release. Star-forming galaxies are typically smaller at longer wavelength from UV-to-NIR at $z<3.5$, especially for more massive galaxies, indicating the inside-out assembly with in-situ star formation if ignoring dust attenuation. The size variation with wavelength shows strong dependence on stellar mass, and shows little or no dependence on redshift, specific star formation rate and galaxy environment. This suggests that the size growth of star-forming galaxies is a self-regulated process primarily governed by stellar mass. We model size as a function of both mass and redshift simultaneously, obtaining $R_{\rm e} \propto M_*^{0.23} (1+z)^{-1.04}$ at a wavelength of 0.45 ${μ\mathrm{m}}$, and $R_{\rm e} \propto M_*^{0.20} (1+z)^{-1.08}$ at 1.0 ${μ\mathrm{m}}$. Based on this size evolution and the star formation main sequence from the literature, we obtain the locus of typical size growth for individual galaxies of different masses on the mass-size plane. The moving trend of galaxies on the mass-size plane, which indicates the slopes of their locus, strongly correlates with the size ratio between 0.45 ${μ\mathrm{m}}$ and 1.0 ${μ\mathrm{m}}$, supporting the idea that the size variation with wavelength provides important information on size growth of galaxies on short timescales.

astro-ph.GA↗

arXiv2024

Enhancing Healthcare through Large Language Models: A Study on Medical Question Answering

In recent years, the application of Large Language Models (LLMs) in healthcare has shown significant promise in improving the accessibility and dissemination of medical knowledge. This paper presents a detailed study of various LLMs trained on the MedQuAD medical question-answering dataset, with a focus on identifying the most effective model for providing accurate medical information. Among the models tested, the Sentence-t5 combined with Mistral 7B demonstrated superior performance, achieving a precision score of 0.762. This model's enhanced capabilities are attributed to its advanced pretraining techniques, robust architecture, and effective prompt construction methodologies. By leveraging these strengths, the Sentence-t5 + Mistral 7B model excels in understanding and generating precise medical answers. Our findings highlight the potential of integrating sophisticated LLMs in medical contexts to facilitate efficient and accurate medical knowledge retrieval, thus significantly enhancing patient education and support.

cs.CL↗

arXiv2024

The boundary of cosmic filaments

For decades, the boundary of cosmic filaments have been a subject of debate. In this work, we determine the physically-motivated radii of filaments by constructing stacked galaxy number density profiles around the filament spines. We find that the slope of the profile changes with distance to the filament spine, reaching its minimum at approximately 1 Mpc at z = 0 in both state-of-the-art hydrodynamical simulations and observational data. This can be taken as the average value of the filament radius. Furthermore, we note that the average filament radius rapidly decreases from z = 4 to z = 1, and then slightly increases. Moreover, we find that the filament radius depends on the filament length, the distance from connected clusters, and the masses of the clusters. These results suggest a two-phase formation scenario of cosmic filaments. The filaments experience rapid contraction before z = 1, but their density distribution has remained roughly stable since then. The subsequent mass transport along the filaments to the connected clusters is likely to have contributed to the formation of the clusters themselves.

astro-ph.CO↗

arXiv2024

Revisiting the fundamental metallicity relation with observation and simulation

The gas-phase metallicity of galaxies is regulated by multiple astrophysical processes, which makes it a crucial diagnostic of galaxy formation and evolution. Beyond the fundamental mass-metallicity relation, a debate about the secondary galaxy property to predict the metallicity of galaxies arises. Motivated by this, we systematically examine the relationship between gas-phase metallicity and other galaxy properties, i.e. star formation rate (SFR) and galaxy size, in addition to stellar mass in both observation and simulation. We utilize the data from the MaNGA (Mapping Nearby Galaxies at Apache Point Observatory) survey and the TNG50 simulations. We find that the combination of $M_*/R_{\rm e}^β$ with $β\sim 0.6-1$ is in much stronger correlation to the metallicity than stellar mass alone, regardless of whether the SFR is included or not, in both observation and simulation. This indicates that galaxy size plays a more important role in determining gas-phase metallicity of galaxies than SFR. In addition, the TNG simulation predicts that the SFR, although being a subdominant role, becomes increasingly important in high-$z$ universe. Finally, we speculate that SFR modulates metallicity on the temporal dimension, synchronized with time-varying gas inflows, and galaxy size regulates metallicity on the spatial dimension by affecting the gravitational potential and the mass loading factor.

astro-ph.GA↗

arXiv2024

CREF: An LLM-based Conversational Software Repair Framework for Programming Tutors

Program repair techniques offer cost-saving benefits for debugging within software development and programming education scenarios. With the proven effectiveness of Large Language Models (LLMs) in code-related tasks, researchers have explored their potential for program repair. However, it is crucial to recognize that existing repair benchmarks may have influenced LLM training data, potentially causing data leakage. To evaluate LLMs' realistic repair capabilities, (1) we introduce an extensive, non-crawled benchmark, referred to as TutorCode, comprising 1,239 C++ defect codes and associated information such as tutor guidance, solution description, failing test cases, and the corrected code. Our work assesses the repair performance of 12 LLMs on TutorCode, measuring repair correctness (TOP-5 and AVG-5) and patch precision (RPSR). (2) We then provide a comprehensive investigation into which types of extra information can help LLMs improve their performance in repairing defects. Among these types, tutor guidance was found to be the most effective information in enhancing LLM repair capabilities. To fully harness LLMs' conversational capabilities and the benefits of augmented information, (3) we introduce a novel conversational semi-automatic repair framework CREF assisting human tutor. It demonstrates a remarkable AVG-5 improvement of 17.2%-24.6% compared to the baseline, achieving an impressive AVG-5 of 76.6% when utilizing GPT-4. These results highlight the potential for enhancing LLMs' repair capabilities through interactions with tutors and historical conversations involving incorrect responses. The successful application of CREF in a real-world educational setting demonstrates its effectiveness in reducing tutors' workload and improving students' learning experience, while also showcasing its promise for facilitating other software engineering tasks, such as code review.

cs.SE↗

arXiv2022

Prompt-Guided Injection of Conformation to Pre-trained Protein Model

Pre-trained protein models (PTPMs) represent a protein with one fixed embedding and thus are not capable for diverse tasks. For example, protein structures can shift, namely protein folding, between several conformations in various biological processes. To enable PTPMs to produce task-aware representations, we propose to learn interpretable, pluggable and extensible protein prompts as a way of injecting task-related knowledge into PTPMs. In this regard, prior PTPM optimization with the masked language modeling task can be interpreted as learning a sequence prompt (Seq prompt) that enables PTPMs to capture the sequential dependency between amino acids. To incorporate conformational knowledge to PTPMs, we propose an interaction-conformation prompt (IC prompt) that is learned through back-propagation with the protein-protein interaction task. As an instantiation, we present a conformation-aware pre-trained protein model that learns both sequence and interaction-conformation prompts in a multi-task setting. We conduct comprehensive experiments on nine protein datasets. Results confirm our expectation that using the sequence prompt does not hurt PTPMs' performance on sequence-related tasks while incorporating the interaction-conformation prompt significantly improves PTPMs' performance on tasks where conformational knowledge counts. We also show the learned prompts can be combined and extended to deal with new complex tasks.

cs.AI↗

arXiv2021

Strategic Information Revelation in Crowdsourcing Systems Without Verification

We study a crowdsourcing problem where the platform aims to incentivize distributed workers to provide high quality and truthful solutions without the ability to verify the solutions. While most prior work assumes that the platform and workers have symmetric information, we study an asymmetric information scenario where the platform has informational advantages. Specifically, the platform knows more information regarding worker average solution accuracy, and can strategically reveal such information to workers. Workers will utilize the announced information to determine the likelihood that they obtain a reward if exerting effort on the task. We study two types of workers, naive workers who fully trust the announcement, and strategic workers who update prior belief based on the announcement. For naive workers, we show that the platform should always announce a high average accuracy to maximize its payoff. However, this is not always optimal for strategic workers, as it may reduce the credibility of the platform announcement and hence reduce the platform payoff. Interestingly, the platform may have an incentive to even announce an average accuracy lower than the actual value when facing strategic workers. Another counterintuitive result is that the platform payoff may decrease in the number of high accuracy workers.

cs.GT↗

arXiv2020

Learning to Price Vehicle Service with Unknown Demand

It can be profitable for vehicle service providers to set service prices based on users' travel demand on different origin-destination pairs. The prior studies on the spatial pricing of vehicle service rely on the assumption that providers know users' demand. In this paper, we study a monopolistic provider who initially does not know users' demand and needs to learn it over time by observing the users' responses to the service prices. We design a pricing and vehicle supply policy, considering the tradeoff between exploration (i.e., learning the demand) and exploitation (i.e., maximizing the provider's short-term payoff). Considering that the provider needs to ensure the vehicle flow balance at each location, its pricing and supply decisions for different origin-destination pairs are tightly coupled. This makes it challenging to theoretically analyze the performance of our policy. We analyze the gap between the provider's expected time-average payoffs under our policy and a clairvoyant policy, which makes decisions based on complete information of the demand. We prove that after running our policy for D days, the loss in the expected time-average payoff can be at most O((ln D)^0.5 D^(-0.25)), which decays to zero as D approaches infinity.

cs.GT↗

arXiv2019

Monetizing Mobile Data via Data Rewards

Most mobile network operators generate revenues by directly charging users for data plan subscriptions. Some operators now also offer users data rewards to incentivize them to watch mobile ads, which enables the operators to collect payments from advertisers and create new revenue streams. In this work, we analyze and compare two data rewarding schemes: a Subscription-Aware Rewarding (SAR) scheme and a Subscription-Unaware Rewarding (SUR) scheme. Under the SAR scheme, only the subscribers of the operators' data plans are eligible for the rewards; under the SUR scheme, all users are eligible for the rewards (e.g., the users who do not subscribe to the data plans can still get SIM cards and receive data rewards by watching ads). We model the interactions among an operator, users, and advertisers by a two-stage Stackelberg game, and characterize their equilibrium strategies under both the SAR and SUR schemes. We show that the SAR scheme can lead to more subscriptions and a higher operator revenue from the data market, while the SUR scheme can lead to better ad viewership and a higher operator revenue from the ad market. We further show that the operator's optimal choice between the two schemes is sensitive to the users' data consumption utility function and the operator's network capacity. We provide some counter-intuitive insights. For example, when each user has a logarithmic utility function, the operator should apply the SUR scheme (i.e., reward both subscribers and non-subscribers) if and only if it has a small network capacity.

cs.GT↗

arXiv2019

Pricing for Collaboration Between Online Apps and Offline Venues

An increasing number of mobile applications (abbrev. apps), like Pokemon Go and Snapchat, reward the users who physically visit some locations tagged as POIs (places-of-interest) by the apps. We study the novel POI-based collaboration between apps and venues (e.g., restaurants). On the one hand, an app charges a venue and tags the venue as a POI. The POI tag motivates users to visit the venue, which potentially increases the venue's sales. On the other hand, the venue can invest in the app-related infrastructure, which enables more users to use the app and further benefits the app's business. The apps' existing POI tariffs cannot fully incentivize the venue's infrastructure investment, and hence cannot lead to the most effective app-venue collaboration. We design an optimal two-part tariff, which charges the venue for becoming a POI, and subsidizes the venue every time a user interacts with the POI. The subsidy design efficiently incentivizes the venue's infrastructure investment, and we prove that our tariff achieves the highest app's revenue among a general class of tariffs. Furthermore, we derive some counter-intuitive guidelines for the POI-based collaboration. For example, a bandwidth-consuming app should collaborate with a low-quality venue (users have low utilities when consuming the venue's products).

cs.GT↗

arXiv2019

Analyzing Location-Based Advertising for Vehicle Service Providers Using Effective Resistances

Vehicle service providers can display commercial ads in their vehicles based on passengers' origins and destinations to create a new revenue stream. In this work, we study a vehicle service provider who can generate different ad revenues when displaying ads on different arcs (i.e., origin-destination pairs). The provider needs to ensure the vehicle flow balance at each location, which makes it challenging to analyze the provider's vehicle assignment and pricing decisions for different arcs. For example, the provider's price for its service on an arc depends on the ad revenues on other arcs as well as on the arc in question. To tackle the problem, we show that the traffic network corresponds to an electrical network. When the effective resistance between two locations is small, there are many paths between the two locations and the provider can easily route vehicles between them. We characterize the dependence of an arc's optimal price on any other arc's ad revenue using the effective resistances between these two arcs' origins and destinations. Furthermore, we study the provider's optimal selection of advertisers when it can only display ads for a limited number of advertisers. If each advertiser has one target arc for advertising, the provider should display ads for the advertiser whose target arc has a small effective resistance. We investigate the performance of our advertiser selection strategy based on a real-world dataset.

cs.GT↗

arXiv2017

Public Wi-Fi Monetization via Advertising

The proliferation of public Wi-Fi hotspots has brought new business potentials for Wi-Fi networks, which carry a significant amount of global mobile data traffic today. In this paper, we propose a novel Wi-Fi monetization model for venue owners (VOs) deploying public Wi-Fi hotspots, where the VOs can generate revenue by providing two different Wi-Fi access schemes for mobile users (MUs): (i) the premium access, in which MUs directly pay VOs for their Wi-Fi usage, and (ii) the advertising sponsored access, in which MUs watch advertisements in exchange of the free usage of Wi-Fi. VOs sell their ad spaces to advertisers (ADs) via an ad platform, and share the ADs' payments with the ad platform. We formulate the economic interactions among the ad platform, VOs, MUs, and ADs as a three-stage Stackelberg game. In Stage I, the ad platform announces its advertising revenue sharing policy. In Stage II, VOs determine the Wi-Fi prices (for MUs) and advertising prices (for ADs). In Stage III, MUs make access choices and ADs purchase advertising spaces. We analyze the sub-game perfect equilibrium (SPE) of the proposed game systematically, and our analysis shows the following useful observations. First, the ad platform's advertising revenue sharing policy in Stage I will affect only the VOs' Wi-Fi prices but not the VOs' advertising prices in Stage II. Second, both the VOs' Wi-Fi prices and advertising prices are non-decreasing in the advertising concentration level and non-increasing in the MU visiting frequency. Numerical results further show that the VOs are capable of generating large revenues through mainly providing one type of Wi-Fi access (the premium access or advertising sponsored access), depending on their advertising concentration levels and MU visiting frequencies.

cs.GT↗

arXiv2016

Auction-Based Coopetition between LTE Unlicensed and Wi-Fi

Motivated by the recent efforts in extending LTE to the unlicensed spectrum, we propose a novel spectrum sharing framework for the coopetition (i.e., cooperation and competition) between LTE and Wi-Fi in the unlicensed band. Basically, the LTE network can choose to work in one of the two modes: in the competition mode, it randomly accesses an unlicensed channel, and interferes with the Wi-Fi access point using the same channel; in the cooperation mode, it delivers traffic for the Wi-Fi users in exchange for the exclusive access of the corresponding channel. Because the LTE network works in an interference-free manner in the cooperation mode, it can achieve a much larger data rate than that in the competition mode, which allows it to effectively serve both its own users and the Wi-Fi users. We design a second-price reverse auction mechanism, which enables the LTE provider and the Wi-Fi access point owners (APOs) to effectively negotiate the operation mode. Specifically, the LTE provider is the auctioneer (buyer), and the APOs are the bidders (sellers) who compete to sell their channel access opportunities to the LTE provider. In Stage I of the auction, the LTE provider announces a reserve rate. In Stage II of the auction, the APOs submit their bids. We show that the auction involves allocative externalities, i.e., the cooperation between the LTE provider and one APO benefits other APOs who are not directly involved in this cooperation. As a result, a particular APO's willingness to cooperate is affected by its belief about other APOs' willingness to cooperate. This makes our analysis much more challenging than that of the conventional second-price auction, where bidding truthfully is a weakly dominant strategy. We show that the APOs have a unique form of the equilibrium bidding strategies in Stage II, based on which we analyze the LTE provider's optimal reserve rate in Stage I.

cs.GT↗

arXiv2016

Cooperative Wi-Fi Deployment: A One-to-Many Bargaining Framework

We study the cooperation of the mobile network operator (MNO) and the venue owners (VOs) on the public Wi-Fi deployment. We consider a one-to-many bargaining framework, where the MNO bargains with VOs sequentially to determine where to deploy Wi-Fi and how much to pay. Taking into account the negative externalities among different steps of bargaining, we analyze the following two cases: for the exogenous bargaining sequence case, we compute the optimal bargaining solution on the cooperation decisions and payments under a predetermined bargaining sequence; for the endogenous bargaining sequence case, the MNO decides the bargaining sequence to maximize its payoff. Through exploring the structural property of the optimal bargaining sequence, we design a low-complexity Optimal VO Bargaining Sequencing (OVBS) algorithm to search the optimal sequence. More specifically, we categorize the VOs into three types based on the impact of the Wi-Fi deployment at their venues, and show that it is optimal for the MNO to bargain with these three types of VOs sequentially. Numerical results show that compared with the random and worst bargaining sequences, the optimal bargaining sequence improves the MNO's payoff by up to 14.8% and 45.3%, respectively.

cs.GT↗

arXiv2015

Power-Delay Tradeoff with Predictive Scheduling in Integrated Cellular and Wi-Fi Networks

The explosive growth of global mobile traffic has lead to a rapid growth in the energy consumption in communication networks. In this paper, we focus on the energy-aware design of the network selection, subchannel, and power allocation in cellular and Wi-Fi networks, while taking into account the traffic delay of mobile users. The problem is particularly challenging due to the two-timescale operations for the network selection (large timescale) and subchannel and power allocation (small timescale). Based on the two-timescale Lyapunov optimization technique, we first design an online Energy-Aware Network Selection and Resource Allocation (ENSRA) algorithm. The ENSRA algorithm yields a power consumption within O(1/V) bound of the optimal value, and guarantees an O(V) traffic delay for any positive control parameter V. Motivated by the recent advancement in the accurate estimation and prediction of user mobility, channel conditions, and traffic demands, we further develop a novel predictive Lyapunov optimization technique to utilize the predictive information, and propose a Predictive Energy-Aware Network Selection and Resource Allocation (P-ENSRA) algorithm. We characterize the performance bounds of P-ENSRA in terms of the power-delay tradeoff theoretically. To reduce the computational complexity, we finally propose a Greedy Predictive Energy-Aware Network Selection and Resource Allocation (GP-ENSRA) algorithm, where the operator solves the problem in P-ENSRA approximately and iteratively. Numerical results show that GP-ENSRA significantly improves the power-delay performance over ENSRA in the large delay regime. For a wide range of system parameters, GP-ENSRA reduces the traffic delay over ENSRA by 20~30% under the same power consumption.

cs.NI↗