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1. EmpatheticFIG at WASSA 2024 Empathy and Personality Shared Task: Predicting Empathy and Emotion in Conversations with Figurative Language

   Lee, Gyeongeun; Wang, Zhu; Ravi, Sathya N; Parde, Natalie (August 2024, Proceedings of the 14th Workshop on Computational Approaches to Subjectivity, Sentiment, & Social Media Analysis)

   Recent research highlights the importance of figurative language as a tool for amplifying emotional impact. In this paper, we dive deeper into this phenomenon and outline our methods for Track 1, Empathy Prediction in Conversations (CONV-dialog) and Track 2, Empathy and Emotion Prediction in Conversation Turns (CONV-turn) of the WASSA 2024 shared task. We leveraged transformer-based large language models augmented with figurative language prompts, specifically idioms, metaphors and hyperbole, that were selected and trained for each track to optimize system performance. For Track 1, we observed that a fine-tuned BERT with metaphor and hyperbole features outperformed other models on the development set. For Track 2, DeBERTa, with different combinations of figurative language prompts, performed well for different prediction tasks. Our method provides a novel framework for understanding how figurative language influences emotional perception in conversational contexts. Our system officially ranked 4th in the 1st track and 3rd in the 2nd track. 

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2. A First Look at River Discharge Estimation From SWOT Satellite Observations

   https://doi.org/10.1029/2024GL114185  

   Andreadis, Konstantinos M; Coss, Steve P; Durand, Michael; Gleason, Colin J; Simmons, Travis T; Tebaldi, Nikki; Bjerklie, David M; Brinkerhoff, Craig; Dudley, Robert W; Gejadze, Igor; et al (May 2025, Geophysical Research Letters)

   Abstract The Surface Water and Ocean Topography (SWOT) satellite has the potential to transform global hydrologic science by offering simultaneous and synoptic estimates of river discharge and other hydraulic variables. Discharge is estimated from SWOT observations of water surface elevation, width, and slope. A first assessment using just the highest quality SWOT measurements, over the first 15 months (March 2023–July 2024) of the mission evaluated at 65 gauged reaches shows results consistent with pre‐launch expectations. SWOT estimates track discharge dynamics without relying on any gauge information: median correlation is 0.73, with a correlation interquartile range of 0.51–0.89. SWOT estimates capture discharge magnitude correctly in some cases but are biased (median bias is 50%) in others. There are already a total of 11,274 ungauged global locations with highest quality SWOT measurements where SWOT discharge is expected to accurately track discharge variations: this value will increase as SWOT data record length grows, algorithms are refined and SWOT measurements are reprocessed. This first look indicates that SWOT discharge is performing as expected for SWOT data that achieve performance requirements, providing observed information on discharge variations in ungauged basins globally. 

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3. Springtime Orthomosaic Images and Digital Surface Models for Beaver Dams and Ponds, northern Baldwin Peninsula, Alaska, April 2024

   https://doi.org/10.18739/A29C6S30W  

   Jones, Benjamin; Tape, Kenneth (January 2024, NSF Arctic Data Center)

   The Arctic Beaver Observation Network (A-BON): Tracking a new disturbance regime project observes beaver engineering across circumarctic treeline and tundra environments during the last half-century by mapping and tracking beaver ponds using remote sensing imagery. Drones are being used to collect baseline data and track beaver dam building and pond evolution over time. This dataset consists of orthomosaic images and digital surface models (DSMs) derived from drone surveys on 02 April and 09 April 2024 for three beaver dam and beaver pond sites (Kotz3, BWest, and BEast) on the Baldwin Peninsula, Alaska. Digital images were acquired from a DJI Phantom 4 Real-Time Kinematic (DJI P4RTK) quadcopter with a DJI D-RTK 2 Mobile Base Station. The drone system was flown at 120 meters (m) above ground level (agl) and flight speeds varied from 8-9 meters/second (m/s). The orientation of the camera was set to 90 degrees (i.e. looking straight down). The along-track overlap and across-track overlap of the mission were set at 80% and 70%, respectively. All images were processed in the software Pix4D Mapper (v. 4.8.4) using the standard 3D Maps workflow and the accurate geolocation and orientation calibration method to produce the orthophoto mosaic and digital surface model at spatial resolutions of 5 and 10 centimeters (cm), respectively. Elevation information represents the snow-covered surface. A Leica Viva differential global positioning system (GPS) provided ground control for the mission and the data were post-processed to WGS84 UTM Zone 3 North in Ellipsoid Heights (meters). 

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4. Search for Nearly Mass-Degenerate Higgsinos Using Low-Momentum Mildly Displaced Tracks in $pp$ Collisions at $\sqrt{s}=13\mathrm{TeV}$ with the ATLAS Detector

   https://doi.org/10.1103/PhysRevLett.132.221801  

   Aad, G; Aakvaag, E; Abbott, B; Abeling, K; Abicht, N J; Abidi, S H; Aboelela, M; Aboulhorma, A; Abramowicz, H; Abreu, H; et al (May 2024, Physical Review Letters)

   Higgsinos with masses near the electroweak scale can solve the hierarchy problem and provide a dark matter candidate, while detecting them at the LHC remains challenging if their mass splitting is $\mathcal{O}\left(1\mathrm{GeV}\right)$. This Letter presents a novel search for nearly mass-degenerate Higgsinos in events with an energetic jet, missing transverse momentum, and a low-momentum track with a significant transverse impact parameter using $140{\mathrm{fb}}^{-1}$of proton-proton collision data at $\sqrt{s}=13\mathrm{TeV}$collected by the ATLAS experiment. For the first time since LEP, a range of mass splittings between the lightest charged and neutral Higgsinos from 0.3 to 0.9 GeV is excluded at 95% confidence level, with a maximum reach of approximately 170 GeV in the Higgsino mass. © 2024 CERN, for the ATLAS Collaboration2024CERN 

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5. DataDreamer: A Tool for Synthetic Data Generation and Reproducible LLM Workflows

   https://doi.org/10.18653/v1/2024.acl-long.208  

   Patel, Ajay; Raffel, Colin; Callison-Burch, Chris (January 2024, Association for Computational Linguistics)

   Large language models (LLMs) have become a dominant and important tool for NLP researchers in a wide range of tasks. Today, many researchers use LLMs in synthetic data generation, task evaluation, fine-tuning, distillation, and other model-in-the-loop research workflows. However, challenges arise when using these models that stem from their scale, their closed source nature, and the lack of standardized tooling for these new and emerging workflows. The rapid rise to prominence of these models and these unique challenges has had immediate adverse impacts on open science and on the reproducibility of work that uses them. In this ACL 2024 theme track paper, we introduce DataDreamer, an open source Python library that allows researchers to write simple code to implement powerful LLM workflows. DataDreamer also helps researchers adhere to best practices that we propose to encourage open science and reproducibility. The library and documentation are available at: https://github.com/datadreamer-dev/DataDreamer. 

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