arXiv subjects

Brendan Cross

Publications and source records attributed to Brendan Cross.

7 recordsLinked to original sources

arXiv2026

Thermodynamic Driving Force Activated Phonon Scattering in InN

Defect related disorder during InN growth is a major challenge for making high performance electronic and optoelectronic devices. This is partly because film quality is often described using reactor specific settings instead of general physical variables. In this study, we show that plasma assisted MOCVD growth of InN can be described using a single thermodynamic driving force coordinate. This coordinate brings together growth kinetics, defect sensitive Raman response and structural coherence across different process conditions. When we use this coordinate, the incorporation rate follows a universal activated trend with a kinetic scale of about 0.08 eV. Raman measurements show a clear crossover between a defect sparse and a defect rich regime, a disorder activated Raman metric increases quickly after the crossover, while an A1-LO control metric stays mostly the same. This suggests that short range lattice disorder, not long range polar coupling, dominates the defect activation process. X-ray diffraction shows that the out of plane coherence length stays the same for samples with the same driving force, even if reactor settings are very different. This supports the idea that structural coherence is organized by thermodynamics in this growth window. Finally, a simple kinetic Monte Carlo model using driving force biased incorporation and defect activation events matches the observed exponential trends and the two regimes, supporting the driving force approach. These results show that a transferable driving force coordinate can be used for plasma assisted InN growth and offer a quantitative way to achieve defect sparse growth conditions.

cond-mat.mtrl-sci↗

arXiv2025

An experimental study of the influence of anonymous information on social media users

Increasingly, people use social media for their day-to-day interactions and as a source of information, even though much of this information is practically anonymous. This raises the question: does anonymous information influence its recipients? We conducted an online, two-phase, preregistered experiment using a nationally representative sample of participants from the U.S. to find the answer. To avoid biases of opinions among participants, in the first phase, each participant examines ten Rorschach inkblots and chooses one of four opinions assigned to each inkblot. In the second phase, the participants are randomly assigned to one of four distinct information conditions and are asked to revisit their opinions for the same ten inkblots. Conditions ranged from repeating phase one to receiving anonymous comments about certain opinions. Results were consistent with the preregistration. Importantly, anonymous comments shown in phase two influence up to half of the participants' opinion selections. To better understand the role of anonymous comments in influencing the selections of opinions, we implemented agent-based modeling (ABM). ABM results suggest that a straightforward mechanism can explain the impact of such information. Overall, our results indicate that even anonymous information can have a significant impact on its recipients, potentially altering their popularity rankings. However, the strength of such influence weakens when recipients' confidence in their selections increases. Additionally, we found that participants' confidence in the first phase is inversely related to the number of change opinions.

cs.SI↗

arXiv2025

Hierarchical community detection benchmark for heterogeneous inter-community connectivity

Here, we introduce a new tool for community detection, a generator of networks, which uses parameters to control the structure of created networks. Typically, network scientists designing novel community detection algorithms use synthetically generated benchmarks with community structures that they intend to detect and scale the benchmark networks across size and density. Currently, available benchmarks use generators limited to the properties of the LFR and GLFR networks. We improve on these previous benchmarks with a new hierarchical benchmark, the HGLFR, that preserves the properties of the LFR and GLFR while extending them to include heterogeneous inter-community connectivity. Networks generated by this benchmark are shown to produce networks with structures triggering the resolution limit while maintaining assortative connectivity.

cs.SI↗

arXiv2024

Limits of Large Language Models in Debating Humans

Large Language Models (LLMs) have shown remarkable promise in communicating with humans. Their potential use as artificial partners with humans in sociological experiments involving conversation is an exciting prospect. But how viable is it? Here, we rigorously test the limits of agents that debate using LLMs in a preregistered study that runs multiple debate-based opinion consensus games. Each game starts with six humans, six agents, or three humans and three agents. We found that agents can blend in and concentrate on a debate's topic better than humans, improving the productivity of all players. Yet, humans perceive agents as less convincing and confident than other humans, and several behavioral metrics of humans and agents we collected deviate measurably from each other. We observed that agents are already decent debaters, but their behavior generates a pattern distinctly different from the human-generated data.

cs.AI↗

arXiv2023

Divide-and-rule policy in the Naming Game

The Naming Game is a classic model for studying the emergence and evolution of language within a population. In this paper, we extend the traditional Naming Game model to encompass multiple committed opinions and investigate the system dynamics on the complete graph with an arbitrarily large population and random networks of finite size. For the fully connected complete graph, the homogeneous mixing condition enables us to use mean-field theory to analyze the opinion evolution of the system. However, when the number of opinions increases, the number of variables describing the system grows exponentially. To mitigate this, we focus on a special scenario where the largest group of committed agents competes with a motley of committed groups, each of which is smaller than the largest one, while initially, most of uncommitted agents hold one unique opinion. This scenario is chosen for its recurrence in diverse societies and its potential for complexity reduction by unifying agents from smaller committed groups into one category. Our investigation reveals that when the size of the largest committed group reaches the critical threshold, most of uncommitted agents change their beliefs to this opinion, triggering a phase transition. Further, we derive the general formula for the multi-opinion evolution using a recursive approach, enabling investigation into any scenario. Finally, we employ agent-based simulations to reveal the opinion evolution and dominance transition in random graphs. Our results provide insights into the conditions under which the dominant opinion emerges in a population and the factors that influence these conditions.

cs.SI↗

arXiv2021

Shifting Polarization and Twitter News Influencers between two U.S. Presidential Elections

Social media are decentralized, interactive, and transformative, empowering users to produce and spread information to influence others. This has changed the dynamics of political communication that were previously dominated by traditional corporate news media. Having hundreds of millions of tweets collected over the 2016 and 2020 U.S. presidential elections gave us a unique opportunity to measure the change in polarization and the diffusion of political information. We analyze the diffusion of political information among Twitter users and investigate the change of polarization between these elections and how this change affected the composition and polarization of influencers and their retweeters. We identify "influencers" by their ability to spread information and classify them into those affiliated with a media organization, a political organization, or unaffiliated. Most of the top influencers were affiliated with media organizations during both elections. We found a clear increase from 2016 to 2020 in polarization among influencers and among those whom they influence. Moreover, 75% of the top influencers in 2020 were not present in 2016, demonstrating that such status is difficult to retain. Between 2016 and 2020, 10% of influencers affiliated with media were replaced by center- or right-orientated influencers affiliated with political organizations and unaffiliated influencers.

cs.SI↗

arXiv2020

Asymptotic resolution bounds of generalized modularity and multi-scale community detection

The maximization of generalized modularity performs well on networks in which the members of all communities are statistically indistinguishable from each other. However, there is no theory bounding the maximization performance in more realistic networks where edges are heterogeneously distributed within and between communities. Using the random graph properties, we establish asymptotic theoretical bounds on the resolution parameter for which the generalized modularity maximization performs well. From this new perspective on random graph model, we find the resolution limit of modularity maximization can be explained in a surprisingly simple and straightforward way. Given a network produced by the stochastic block models, the communities for which the resolution parameter is larger than their densities are likely to be spread among multiple clusters, while communities for which the resolution parameter is smaller than their background inter-community edge density will be merged into one large component. Therefore, no suitable resolution parameter exits when the intra-community edge density in a subgraph is lower than the inter-community edge density in some other subgraph. For such networks, we propose a progressive agglomerative heuristic algorithm to detect practically significant communities at multiple scales.

cs.SI↗