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Many environmental, energy, and industrial processes involve the flow of polymer solutions in three-dimensional (3D) porous media where fluid is confined to navigate through complex pore space geometries. As polymers are transported through the tortuous pore space, elastic stresses accumulate, leading to the onset of unsteady flow fluctuations above a threshold flow rate. How does pore space geometry influence the development and features of this elastic instability? Here, we address this question by directly imaging polymer solution flow in microfabricated 3D ordered porous media with precisely controlled geometries consisting of simple-cubic (SC) or body-centered cuboid (BC) arrays of spherical grains. In both cases, we find that the flow instability is generated at stagnation points arising at the contacts between grains rather than at the polar upstream/downstream grain surfaces, as is the case for flow around a single grain. The characteristics of the flow instability are strongly dependent on the unit cell geometry: in SC packings, the instability manifests through the formation of time-dependent, fluctuating 3D eddies, whereas in BC packings, it manifests as continual fluctuating 'wobbles' and crossing in the flow pathlines. Despite this difference, we find that characteristics of the transition from steady to unsteady flow with increasing flow rate have commonalities across geometries. Moreover, for both packing geometries, our data indicate that extensional flow-induced polymeric stresses generated by contact-associated stagnation points are the primary contributor to the macroscopic resistance to flow across the entire medium. Altogether, our work highlights the pivotal role of inter-grain contacts -- which are typically idealized as discrete points and therefore overlooked, but are inherent in most natural and engineered media -- in shaping elastic instabilities in porous media.

This paper explores null elements in English, Chinese, and Korean Penn treebanks. Null elements contain important syntactic and semantic information, yet they have typically been treated as entities to be removed during language processing tasks, particularly in constituency parsing. Thus, we work towards the removal and, in particular, the restoration of null elements in parse trees. We focus on expanding a rule-based approach utilizing linguistic context information to Chinese, as rule based approaches have historically only been applied to English. We also worked to conduct neural experiments with a language agnostic sequence-to-sequence model to recover null elements for English (PTB), Chinese (CTB) and Korean (KTB). To the best of the authors' knowledge, null elements in three different languages have been explored and compared for the first time. In expanding a rule based approach to Chinese, we achieved an overall F1 score of 80.00, which is comparable to past results in the CTB. In our neural experiments we achieved F1 scores up to 90.94, 85.38 and 88.79 for English, Chinese, and Korean respectively with functional labels., Comment: 10 pages

Despite decades of research and development, the optimal efficiency of slurry-packed HPLC columns is still hindered by inherent long-range flow heterogeneity from the wall to the central bulk region of these columns. Here, we show an example of how this issue can be addressed through the straightforward addition of a semidilute amount (500~ppm) of a large, flexible, synthetic polymer (18~MDa partially hydrolyzed polyacrylamide, HPAM) to the mobile phase (1\% NaCl aqueous solution) during operation of a 4.6 mm $\times$ 300 mm column packed with 10~$\mu$m BEH$^{\mathrm{TM}}$ 125~\AA \ Particles. Addition of the polymer imparts elasticity to the mobile phase, causing the flow in the interparticle pore space to become unstable above a threshold flow rate. We verify the development of this elastic flow instability using pressure drop measurements of the friction factor versus Reynolds number. In prior work, we showed that this flow instability is characterized by large spatiotemporal fluctuations in the pore-scale flow velocities that may promote analyte dispersion across the column. Axial dispersion measurements of the quasi non-retained tracer thiourea confirm this possibility: they unequivocally reveal that operating above the onset of the instability improves column efficiency by significantly reducing peak asymmetry. These experiments thereby provide a proof-of-concept demonstration that elastic flow instabilities can be harnessed to mitigate the negative impact of trans-column flow heterogeneities on the efficiency of slurry-packed HPLC columns. While this approach has its own inherent limitations and constraints, our work lays the groundwork for future targeted development of polymers that can impart elasticity when dissolved in commonly used liquid chromatography mobile phases, and can thereby generate elastic flow instabilities to help improve the resolution of HPLC columns.

Diverse chemical, energy, environmental, and industrial processes involve the flow of polymer solutions in porous media. The accumulation and dissipation of elastic stresses as the polymers are transported through the tortuous, confined pore space can lead to the development of an elastic flow instability above a threshold flow rate. This flow instability can generate complex flows with strong spatiotemporal fluctuations, despite the low Reynolds number ($\mathrm{Re} \ll 1$); for example, in 1D ordered arrays of pore constrictions, this unstable flow can be multistable, with distinct pores exhibiting distinct unstable flow states. Here, we examine how this multistability is influenced by fluid rheology. Through experiments using diverse polymer solutions having systematic variations in fluid shear-thinning or elasticity, in pore constriction arrays of varying geometries, we show that the onset of multistability can be described using a single dimensionless parameter. This parameter, the streamwise Deborah number, compares the stress relaxation time of the polymer solution to the time required for the fluid to be advected between pore constrictions. Our work thus helps to deepen understanding of the influence of fluid rheology on elastic instabilities, helping to establish guidelines for the rational design of polymeric fluids with desirable flow behaviors.

Can computers perceive the physical properties of objects solely through vision? Research in cognitive science and vision science has shown that humans excel at identifying materials and estimating their physical properties based purely on visual appearance. In this paper, we present a novel approach for dense prediction of the physical properties of objects using a collection of images. Inspired by how humans reason about physics through vision, we leverage large language models to propose candidate materials for each object. We then construct a language-embedded point cloud and estimate the physical properties of each 3D point using a zero-shot kernel regression approach. Our method is accurate, annotation-free, and applicable to any object in the open world. Experiments demonstrate the effectiveness of the proposed approach in various physical property reasoning tasks, such as estimating the mass of common objects, as well as other properties like friction and hardness., Comment: CVPR 2024. Project page (with code): https://ajzhai.github.io/NeRF2Physics/

This study investigates the concept of flexibility within League of Legends, a popular online multiplayer game, focusing on the relationship between user adaptability and team success. Utilizing a dataset encompassing players of varying skill levels and play styles, we calculate two measures of flexibility for each player: overall flexibility and temporal flexibility. Our findings suggest that the flexibility of a user is dependent upon a user's preferred play style, and flexibility does impact match outcome. This work also shows that skill level not only indicates how willing a player is to adapt their play style but also how their adaptability changes over time. This paper highlights the duality and balance of specialization versus flexibility, providing insights that can inform strategic planning, collaboration and resource allocation in competitive environments.

We present the vibrational properties and phonon dispersion for quasi-2D hybrid organic-inorganic perovskites (BA)$_2$CsPb$_2$I$_7$, (HA)$_2$CsPb$_2$I$_7$, (BA)$_2$(MA)Pb$_2$I$_7$, and (HA)$_2$(MA)Pb$_2$I$_7$ calculated from first principles. Given the highly complex nature of these compounds, we first perform careful benchmarking and convergence testing to identify suitable parameters to describe their structural features and vibrational properties. We find that the inclusion of van der Waals corrections on top of generalized gradient approximation (GGA) exchange-correlation functionals provides the best agreement for the equilibrium structure relative to experimental data. We then compute vibrational properties under the harmonic approximation. We find that stringent energy cut-offs are required to obtain well-converged phonon properties, and once converged, the harmonic approximation can capture key physics for such a large, hybrid inorganic-organic system with vastly different atom types, masses, and interatomic interactions. We discuss the obtained phonon modes and dispersion behavior in the context of known properties for bulk 3D perovskites and ligand molecular crystals. While many vibrational properties are inherited from the parent systems, we also observe unique coupled vibrations that cannot be associated with vibrations of the pure constituent perovskite and ligand subphases. Dispersive low energy phonon branches primarily occur in the in-plane direction and within the perovskite subphase, and arise from bending and breathing modes of the equatorial Pb-I network within the perovskite octahedral plane.

This paper presents a cost-effective, low-power approach to unintentional fall detection using knowledge distillation-based LSTM (Long Short-Term Memory) models to significantly improve accuracy. With a primary focus on analyzing time-series data collected from various sensors, the solution offers real-time detection capabilities, ensuring prompt and reliable identification of falls. The authors investigate fall detection models that are based on different sensors, comparing their accuracy rates and performance. Furthermore, they employ the technique of knowledge distillation to enhance the models' precision, resulting in refined accurate configurations that consume lower power. As a result, this proposed solution presents a compelling avenue for the development of energy-efficient fall detection systems for future advancements in this critical domain., Comment: 4 pages

Social media moderation policies are often at the center of public debate, and their implementation and enactment are sometimes surrounded by a veil of mystery. Unsurprisingly, due to limited platform transparency and data access, relatively little research has been devoted to characterizing moderation dynamics, especially in the context of controversial events and the platform activity associated with them. Here, we study the dynamics of account creation and suspension on Twitter during two global political events: Russia's invasion of Ukraine and the 2022 French Presidential election. Leveraging a large-scale dataset of 270M tweets shared by 16M users in multiple languages over several months, we identify peaks of suspicious account creation and suspension, and we characterize behaviours that more frequently lead to account suspension. We show how large numbers of accounts get suspended within days from their creation. Suspended accounts tend to mostly interact with legitimate users, as opposed to other suspicious accounts, often making unwarranted and excessive use of reply and mention features, and predominantly sharing spam and harmful content. While we are only able to speculate about the specific causes leading to a given account suspension, our findings shed light on patterns of platform abuse and subsequent moderation during major events., Comment: See published version at EPJ Data Science

Stating your gender pronouns, along with your name, is becoming the new norm of self-introductions at school, at the workplace, and online. The increasing prevalence and awareness of nonconforming gender identities put discussions of developing gender-inclusive language at the forefront. This work presents the first empirical research on gender pronoun usage on large-scale social media. Leveraging a Twitter dataset of over 2 billion tweets collected continuously over two years, we find that the public declaration of gender pronouns is on the rise, with most people declaring as using she series pronouns, followed by he series pronouns, and a smaller but considerable amount of non-binary pronouns. From analyzing Twitter posts and sharing activities, we can discern users who use gender pronouns from those who do not and also distinguish users of various gender identities. We further illustrate the relationship between explicit forms of social network exposure to gender pronouns and their eventual gender pronoun adoption. This work carries crucial implications for gender-identity studies and initiates new research directions in gender-related fairness and inclusion, as well as support against online harassment and discrimination on social media., Comment: 11 pages, 7 figures, 1 table

Understanding social interactions and generous behaviors have long been of considerable interest in the social science community. While the contagion of generosity is documented in the real world, less is known about such phenomenon in virtual worlds and whether it has an actionable impact on user behavior and retention. In this work, we analyze social dynamics in the virtual world of the popular massively multiplayer online role-playing game (MMORPG) Sky: Children of Light. We develop a framework to reveal the patterns of generosity in such social environments and provide empirical evidence of social contagion and contagious generosity. Players become more engaged in the game after playing with others and especially with friends. We also find that players who experience generosity first-hand or even observe other players conduct generous acts become more generous themselves in the future. Additionally, we show that both receiving and observing generosity lead to higher future engagement in the game. Since Sky resembles the real world from a social play aspect, the implications of our findings also go beyond this virtual world., Comment: 22 pages, 6 figures, 6 tables. To appear in the Proceedings of the ACM on Human-Computer Interaction (PACM HCI), CSCW 2022

On February 24, 2022, Russia invaded Ukraine. In the days that followed, reports kept flooding in from layman to news anchors of a conflict quickly escalating into war. Russia faced immediate backlash and condemnation from the world at large. While the war continues to contribute to an ongoing humanitarian and refugee crisis in Ukraine, a second battlefield has emerged in the online space, both in the use of social media to garner support for both sides of the conflict and also in the context of information warfare. In this paper, we present a collection of over 63 million tweets, from February 22, 2022 through March 8, 2022 that we are publishing for the wider research community to use. This dataset can be found at https://github.com/echen102/ukraine-russia and will be maintained and regularly updated as the war continues to unfold. Our preliminary analysis already shows evidence of public engagement with Russian state sponsored media and other domains that are known to push unreliable information; the former saw a spike in activity on the day of the Russian invasion. Our hope is that this public dataset can help the research community to further understand the ever evolving role that social media plays in information dissemination, influence campaigns, grassroots mobilization, and much more, during a time of conflict., Comment: Dataset at https://github.com/echen102/ukraine-russia

Epistemic AI accelerates biomedical discovery by finding hidden connections in the network of biomedical knowledge. The Epistemic AI web-based software platform embodies the concept of knowledge mapping, an interactive process that relies on a knowledge graph in combination with natural language processing (NLP), information retrieval, relevance feedback, and network analysis. Knowledge mapping reduces information overload, prevents costly mistakes, and minimizes missed opportunities in the research process. The platform combines state-of-the-art methods for information extraction with machine learning, artificial intelligence and network analysis. Starting from a single biological entity, such as a gene or disease, users may: a) construct a map of connections to that entity, b) map an entire domain of interest, and c) gain insight into large biological networks of knowledge. Knowledge maps provide clarity and organization, simplifying the day-to-day research processes., Comment: 12 pages, 2 main figures

The year 2020 will be remembered for two events of global significance: the COVID-19 pandemic and 2020 U.S. Presidential Election. In this chapter, we summarize recent studies using large public Twitter data sets on these issues. We have three primary objectives. First, we delineate epistemological and practical considerations when combining the traditions of computational research and social science research. A sensible balance should be struck when the stakes are high between advancing social theory and concrete, timely reporting of ongoing events. We additionally comment on the computational challenges of gleaning insight from large amounts of social media data. Second, we characterize the role of social bots in social media manipulation around the discourse on the COVID-19 pandemic and 2020 U.S. Presidential Election. Third, we compare results from 2020 to prior years to note that, although bot accounts still contribute to the emergence of echo-chambers, there is a transition from state-sponsored campaigns to domestically emergent sources of distortion. Furthermore, issues of public health can be confounded by political orientation, especially from localized communities of actors who spread misinformation. We conclude that automation and social media manipulation pose issues to a healthy and democratic discourse, precisely because they distort representation of pluralism within the public sphere., Comment: Book Chapter submitted for the Handbook of Computational Social Science

St. Lawrence Island Yupik (ISO 639-3: ess) is an endangered polysynthetic language in the Inuit-Yupik language family indigenous to Alaska and Chukotka. This work presents a step-by-step pipeline for the digitization of written texts, and the first publicly available digital corpus for St. Lawrence Island Yupik, created using that pipeline. This corpus has great potential for future linguistic inquiry and research in NLP. It was also developed for use in Yupik language education and revitalization, with a primary goal of enabling easy access to Yupik texts by educators and by members of the Yupik community. A secondary goal is to support development of language technology such as spell-checkers, text-completion systems, interactive e-books, and language learning apps for use by the Yupik community., Comment: ComputEL-4

The novel coronavirus pandemic continues to ravage communities across the US. Opinion surveys identified importance of political ideology in shaping perceptions of the pandemic and compliance with preventive measures. Here, we use social media data to study complexity of polarization. We analyze a large dataset of tweets related to the pandemic collected between January and May of 2020, and develop methods to classify the ideological alignment of users along the moderacy (hardline vs moderate), political (liberal vs conservative) and science (anti-science vs pro-science) dimensions. While polarization along the science and political dimensions are correlated, politically moderate users are more likely to be aligned with the pro-science views, and politically hardline users with anti-science views. Contrary to expectations, we do not find that polarization grows over time; instead, we see increasing activity by moderate pro-science users. We also show that anti-science conservatives tend to tweet from the Southern US, while anti-science moderates from the Western states. Our findings shed light on the multi-dimensional nature of polarization, and the feasibility of tracking polarized opinions about the pandemic across time and space through social media data., Comment: 10 pages, 5 figures

The integrity of democratic political discourse is at the core to guarantee free and fair elections. With social media often dictating the tones and trends of politics-related discussion, it is of paramount important to be able to study online chatter, especially in the run up to important voting events, like in the case of the upcoming November 3, 2020 U.S. Presidential Election. Limited access to social media data is often the first barrier to impede, hinder, or slow down progress, and ultimately our understanding of online political discourse. To mitigate this issue and try to empower the Computational Social Science research community, we decided to publicly release a massive-scale, longitudinal dataset of U.S. politics- and election-related tweets. This multilingual dataset that we have been collecting for over one year encompasses hundreds of millions of tweets and tracks all salient U.S. politics trends, actors, and events between 2019 and 2020. It predates and spans the whole period of Republican and Democratic primaries, with real-time tracking of all presidential contenders of both sides of the isle. After that, it focuses on presidential and vice-presidential candidates. Our dataset release is curated, documented and will be constantly updated on a weekly-basis, until the November 3, 2020 election and beyond. We hope that the academic community, computational journalists, and research practitioners alike will all take advantage of our dataset to study relevant scientific and social issues, including problems like misinformation, information manipulation, interference, and distortion of online political discourse that have been prevalent in the context of recent election events in the United States and worldwide. Our dataset is available at: https://github.com/echen102/us-pres-elections-2020, Comment: Our dataset is available at: https://github.com/echen102/us-pres-elections-2020

Recent years have seen a boom in interest in machine learning systems that can provide a human-understandable rationale for their predictions or decisions. However, exactly what kinds of explanation are truly human-interpretable remains poorly understood. This work advances our understanding of what makes explanations interpretable under three specific tasks that users may perform with machine learning systems: simulation of the response, verification of a suggested response, and determining whether the correctness of a suggested response changes under a change to the inputs. Through carefully controlled human-subject experiments, we identify regularizers that can be used to optimize for the interpretability of machine learning systems. Our results show that the type of complexity matters: cognitive chunks (newly defined concepts) affect performance more than variable repetitions, and these trends are consistent across tasks and domains. This suggests that there may exist some common design principles for explanation systems., Comment: arXiv admin note: substantial text overlap with arXiv:1802.00682

Recent years have seen a boom in interest in machine learning systems that can provide a human-understandable rationale for their predictions or decisions. However, exactly what kinds of explanation are truly human-interpretable remains poorly understood. This work advances our understanding of what makes explanations interpretable in the specific context of verification. Suppose we have a machine learning system that predicts X, and we provide rationale for this prediction X. Given an input, an explanation, and an output, is the output consistent with the input and the supposed rationale? Via a series of user-studies, we identify what kinds of increases in complexity have the greatest effect on the time it takes for humans to verify the rationale, and which seem relatively insensitive.