Search for: All records

Practitioners dealing with large text collections frequently use topic models such as Latent Dirichlet Allocation (LDA) and Non-negative Matrix Factorization (NMF) in their projects to explore trends. Despite twenty years of accrued advancement in natural language processing tools, these models are found to be slow and challenging to apply to text exploration projects. In our work, we engaged with practitioners (n=15) who use topic modeling to explore trends in large text collections to understand their project workflows and investigate which factors often slow down the processes and how they deal with such errors and interruptions in automated topic modeling. Our findings show that practitioners are required to diagnose and resolve context-specific problems with preparing data and models and need control for these steps, especially for data cleaning and parameter selection. Our major findings resonate with existing work across CSCW, computational social science, machine learning, data science, and digital humanities. They also leave us questioning whether automation is actually a useful goal for tools designed for topic models and text exploration. 

Biological springs can be used in nature for energy conservation and ultra-fast motion. The loading and unloading rates of elastic materials can play an important role in determining how the properties of these springs affect movements. We investigate the mechanical energy efficiency of biological springs (American bullfrog plantaris tendons and guinea fowl lateral gastrocnemius tendons) and synthetic elastomers. We measure these materials under symmetric rates (equal loading and unloading durations) and asymmetric rates (unequal loading and unloading durations) using novel dynamic mechanical analysis measurements. We find that mechanical efficiency is highest at symmetric rates and significantly decreases with a larger degree of asymmetry. A generalized one-dimensional Maxwell model with no fitting parameters captures the experimental results based on the independently characterized linear viscoelastic properties of the materials. The model further shows that a broader viscoelastic relaxation spectrum enhances the effect of rate-asymmetry on efficiency. Overall, our study provides valuable insights into the interplay between material properties and unloading dynamics in both biological and synthetic elastic systems. 

Electric double layers are crucial to energy storage and electrocatalytic device performance. While double layer formation originates in electrostatic interactions, electric double layer properties are governed by a balance of both electrostatic and entropic driving forces; favorable ion-surface electrostatic interactions attract counterions to charged surfaces to compensate, or "screen," potentials, but the confinement of these same ions from a bulk reservoir to the interface incurs an entropic penalty. Here, we use a dicationic imidazolium ionic liquid and its monovalent analogue to explore how cation valence and entropy influence double layer formation and electrochemical reactivity using CO2 electroreduction as a model reaction. We find that divalent and monovalent cations display similar CO2 reduction kinetics but differ vastly in steady-state reactivity due to rapid electrochemically induced precipitation of insulating dicationic (bi)carbonate films. Using in situ surface-enhanced Raman scattering spectroscopy, we find that potential-dependent reorientation occurs at similar potentials between the two ionic liquids, but the introduction of a covalent link in the divalent cation imparts a more ordered double layer structure that favors (bi)carbonate precipitation. In mixed monovalent-divalent electrolytes, we find that the divalent cations dominate interfacial properties by preferentially accumulating at surfaces even at very low relative concentrations. Our findings confirm that ion entropy plays a key role in modulating local electrochemical environments and highlight how double layer properties are very sensitive to the properties of counterions that pay the lowest entropic penalty to accumulate at interfaces. Overall, we illustrate that ion entropy provides a new knob to tune reaction microenvironments and unveil how entropy plays a major role in modulating electrochemical reactivity in mixed ion electrolytes. 

Probabilistic Simple Temporal Networks (PSTN) facilitate solving many interesting scheduling problems by characterizing uncertain task durations with unbounded probabilistic distributions. However, most current approaches assess PSTN performance using normal or uniform distributions of temporal uncertainty. This paper explores how well such approaches extend to families of non-symmetric distributions shown to better represent the temporal uncertainty introduced by, e.g., human teammates by building new PSTN benchmarks. We also build probability-aware variations of current approaches that are more reactive to the shape of the underlying distributions. We empirically evaluate the original and modified approaches over well-established PSTN datasets. Our results demonstrate that alignment between the planning model and reality significantly impacts performance. While our ideas for augmenting existing algorithms to better account for human-style uncertainty yield only marginal gains, our results surprisingly demonstrate that existing methods handle positively-skewed temporal uncertainty better. 

Abstract Aggressive cancers, characterized by high metastatic potential and resistance to conventional therapies, present a significant challenge in oncology. Current treatments often fail to effectively target metastasis, recurrence, and the immunosuppressive tumor microenvironment, while causing significant off‐target toxicity. Here, superparamagnetic copper iron oxide nanoparticles (SCIONs) as a multifunctional platform that integrates magnetic hyperthermia therapy, immune modulation, and targeted chemotherapeutic delivery, aiming to provide a more comprehensive cancer treatment is presented. Specifically, SCIONs generate localized hyperthermia under an alternating magnetic field while delivering a copper‐based anticancer agent, resulting in a synergistic anticancer effect. The hyperthermia induced by SCIONs caused ER stress and ROS production, leading to significant tumor cell death, while the copper complex further enhanced oxidative stress, ferroptosis, and apoptosis. Beyond direct cytotoxicity, SCIONs disrupted the tumor microenvironment by inhibiting cancer‐associated fibroblasts, downregulating epithelial‐mesenchymal transition markers, and reducing cell migration and invasion, thereby limiting metastasis. Additionally, SCION‐based therapy reprogrammed the immune microenvironment by inducing immunogenic cell death and enhancing dendritic cell activation, resulting in increased CD8+ T cell infiltration and amplified antitumor immunity. This integrated approach targets primary and metastatic tumors while mitigating immunosuppression, offering a promising next‐generation therapy for combating cancer with enhanced efficacy and reduced side effects. 

The ideological asymmetries have been recently observed in contested online spaces, where conservative voices seem to be relatively more pronounced even though liberals are known to have the population advantage on digital platforms. Most prior research, however, focused on either one single platform or one single political topic. Whether an ideological group garners more attention across platforms and/or topics, and how the attention dynamics evolve over time, have not been explored. In this work, we present a quantitative study that links collective attention across two social platforms -- YouTube and Twitter, centered on online activities surrounding popular videos of three controversial political topics including Abortion, Gun control, and Black Lives Matter over 16 months. We propose several sets of video-centric metrics to characterize how online attention is accumulated for different ideological groups. We find that neither side is on a winning streak: left-leaning videos are overall more viewed, more engaging, but less tweeted than right-leaning videos. The attention time series unfold quicker for left-leaning videos, but span a longer time for right-leaning videos. Network analysis on the early adopters and tweet cascades show that the information diffusion for left-leaning videos tends to involve centralized actors; while that for right-leaning videos starts earlier in the attention lifecycle. In sum, our findings go beyond the static picture of ideological asymmetries in digital spaces and provide a set of methods to quantify attention dynamics across different social platforms. 

We present the first large-scale measurement study of cross-partisan discussions between liberals and conservatives on YouTube, based on a dataset of 274,241 political videos from 973 channels of US partisan media and 134M comments from 9.3M users over eight months in 2020. Contrary to a simple narrative of echo chambers, we find a surprising amount of cross-talk: most users with at least 10 comments posted at least once on both left-leaning and right-leaning YouTube channels. Cross-talk, however, was not symmetric. Based on the user leaning predicted by a hierarchical attention model, we find that conservatives were much more likely to comment on left-leaning videos than liberals on right-leaning videos. Secondly, YouTube's comment sorting algorithm made cross-partisan comments modestly less visible; for example, comments from conservatives made up 26.3% of all comments on left-leaning videos but just over 20% of the comments were in the top 20 positions. Lastly, using Perspective API's toxicity score as a measure of quality, we find that conservatives were not significantly more toxic than liberals when users directly commented on the content of videos. However, when users replied to comments from other users, we find that cross-partisan replies were more toxic than co-partisan replies on both left-leaning and right-leaning videos, with cross-partisan replies being especially toxic on the replier's home turf.