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BioCoref: Benchmarking Biomedical Coreference Resolution with LLMs
Authors:
Nourah M Salem,
Elizabeth White,
Michael Bada,
Lawrence Hunter
Abstract:
Coreference resolution in biomedical texts presents unique challenges due to complex domain-specific terminology, high ambiguity in mention forms, and long-distance dependencies between coreferring expressions. In this work, we present a comprehensive evaluation of generative large language models (LLMs) for coreference resolution in the biomedical domain. Using the CRAFT corpus as our benchmark,…
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Coreference resolution in biomedical texts presents unique challenges due to complex domain-specific terminology, high ambiguity in mention forms, and long-distance dependencies between coreferring expressions. In this work, we present a comprehensive evaluation of generative large language models (LLMs) for coreference resolution in the biomedical domain. Using the CRAFT corpus as our benchmark, we assess the LLMs' performance with four prompting experiments that vary in their use of local, contextual enrichment, and domain-specific cues such as abbreviations and entity dictionaries. We benchmark these approaches against a discriminative span-based encoder, SpanBERT, to compare the efficacy of generative versus discriminative methods. Our results demonstrate that while LLMs exhibit strong surface-level coreference capabilities, especially when supplemented with domain-grounding prompts, their performance remains sensitive to long-range context and mentions ambiguity. Notably, the LLaMA 8B and 17B models show superior precision and F1 scores under entity-augmented prompting, highlighting the potential of lightweight prompt engineering for enhancing LLM utility in biomedical NLP tasks.
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Submitted 28 October, 2025;
originally announced October 2025.
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GAPMAP: Mapping Scientific Knowledge Gaps in Biomedical Literature Using Large Language Models
Authors:
Nourah M Salem,
Elizabeth White,
Michael Bada,
Lawrence Hunter
Abstract:
Scientific progress is driven by the deliberate articulation of what remains unknown. This study investigates the ability of large language models (LLMs) to identify research knowledge gaps in the biomedical literature. We define two categories of knowledge gaps: explicit gaps, clear declarations of missing knowledge; and implicit gaps, context-inferred missing knowledge. While prior work has focu…
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Scientific progress is driven by the deliberate articulation of what remains unknown. This study investigates the ability of large language models (LLMs) to identify research knowledge gaps in the biomedical literature. We define two categories of knowledge gaps: explicit gaps, clear declarations of missing knowledge; and implicit gaps, context-inferred missing knowledge. While prior work has focused mainly on explicit gap detection, we extend this line of research by addressing the novel task of inferring implicit gaps. We conducted two experiments on almost 1500 documents across four datasets, including a manually annotated corpus of biomedical articles. We benchmarked both closed-weight models (from OpenAI) and open-weight models (Llama and Gemma 2) under paragraph-level and full-paper settings. To address the reasoning of implicit gaps inference, we introduce \textbf{\small TABI}, a Toulmin-Abductive Bucketed Inference scheme that structures reasoning and buckets inferred conclusion candidates for validation. Our results highlight the robust capability of LLMs in identifying both explicit and implicit knowledge gaps. This is true for both open- and closed-weight models, with larger variants often performing better. This suggests a strong ability of LLMs for systematically identifying candidate knowledge gaps, which can support early-stage research formulation, policymakers, and funding decisions. We also report observed failure modes and outline directions for robust deployment, including domain adaptation, human-in-the-loop verification, and benchmarking across open- and closed-weight models.
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Submitted 28 October, 2025;
originally announced October 2025.
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LVADNet3D: A Deep Autoencoder for Reconstructing 3D Intraventricular Flow from Sparse Hemodynamic Data
Authors:
Mohammad Abdul Hafeez Khan,
Marcello Mattei Di Eugeni,
Benjamin Diaz,
Ruth E. White,
Siddhartha Bhattacharyya,
Venkat Keshav Chivukula
Abstract:
Accurate assessment of intraventricular blood flow is essential for evaluating hemodynamic conditions in patients supported by Left Ventricular Assist Devices (LVADs). However, clinical imaging is either incompatible with LVADs or yields sparse, low-quality velocity data. While Computational Fluid Dynamics (CFD) simulations provide high-fidelity data, they are computationally intensive and impract…
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Accurate assessment of intraventricular blood flow is essential for evaluating hemodynamic conditions in patients supported by Left Ventricular Assist Devices (LVADs). However, clinical imaging is either incompatible with LVADs or yields sparse, low-quality velocity data. While Computational Fluid Dynamics (CFD) simulations provide high-fidelity data, they are computationally intensive and impractical for routine clinical use. To address this, we propose LVADNet3D, a 3D convolutional autoencoder that reconstructs full-resolution intraventricular velocity fields from sparse velocity vector inputs. In contrast to a standard UNet3D model, LVADNet3D incorporates hybrid downsampling and a deeper encoder-decoder architecture with increased channel capacity to better capture spatial flow patterns. To train and evaluate the models, we generate a high-resolution synthetic dataset of intraventricular blood flow in LVAD-supported hearts using CFD simulations. We also investigate the effect of conditioning the models on anatomical and physiological priors. Across various input configurations, LVADNet3D outperforms the baseline UNet3D model, yielding lower reconstruction error and higher PSNR results.
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Submitted 20 September, 2025;
originally announced September 2025.
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Mitigating Domain Drift in Multi Species Segmentation with DINOv2: A Cross-Domain Evaluation in Herbicide Research Trials
Authors:
Artzai Picon,
Itziar Eguskiza,
Daniel Mugica,
Javier Romero,
Carlos Javier Jimenez,
Eric White,
Gabriel Do-Lago-Junqueira,
Christian Klukas,
Ramon Navarra-Mestre
Abstract:
Reliable plant species and damage segmentation for herbicide field research trials requires models that can withstand substantial real-world variation across seasons, geographies, devices, and sensing modalities. Most deep learning approaches trained on controlled datasets fail to generalize under these domain shifts, limiting their suitability for operational phenotyping pipelines. This study eva…
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Reliable plant species and damage segmentation for herbicide field research trials requires models that can withstand substantial real-world variation across seasons, geographies, devices, and sensing modalities. Most deep learning approaches trained on controlled datasets fail to generalize under these domain shifts, limiting their suitability for operational phenotyping pipelines. This study evaluates a segmentation framework that integrates vision foundation models (DINOv2) with hierarchical taxonomic inference to improve robustness across heterogeneous agricultural conditions. We train on a large, multi-year dataset collected in Germany and Spain (2018-2020), comprising 14 plant species and 4 herbicide damage classes, and assess generalization under increasingly challenging shifts: temporal and device changes (2023), geographic transfer to the United States, and extreme sensor shift to drone imagery (2024). Results show that the foundation-model backbone consistently outperforms prior baselines, improving species-level F1 from 0.52 to 0.87 on in-distribution data and maintaining significant advantages under moderate (0.77 vs. 0.24) and extreme (0.44 vs. 0.14) shift conditions. Hierarchical inference provides an additional layer of robustness, enabling meaningful predictions even when fine-grained species classification degrades (family F1: 0.68, class F1: 0.88 on aerial imagery). Error analysis reveals that failures under severe shift stem primarily from vegetation-soil confusion, suggesting that taxonomic distinctions remain preserved despite background and viewpoint variability. The system is now deployed within BASF's phenotyping workflow for herbicide research trials across multiple regions, illustrating the practical viability of combining foundation models with structured biological hierarchies for scalable, shift-resilient agricultural monitoring.
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Submitted 10 April, 2026; v1 submitted 10 August, 2025;
originally announced August 2025.
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BioCLIP 2: Emergent Properties from Scaling Hierarchical Contrastive Learning
Authors:
Jianyang Gu,
Samuel Stevens,
Elizabeth G Campolongo,
Matthew J Thompson,
Net Zhang,
Jiaman Wu,
Andrei Kopanev,
Zheda Mai,
Alexander E. White,
James Balhoff,
Wasila Dahdul,
Daniel Rubenstein,
Hilmar Lapp,
Tanya Berger-Wolf,
Wei-Lun Chao,
Yu Su
Abstract:
Foundation models trained at scale exhibit remarkable emergent behaviors, learning new capabilities beyond their initial training objectives. We find such emergent behaviors in biological vision models via large-scale contrastive vision-language training. To achieve this, we first curate TreeOfLife-200M, comprising 214 million images of living organisms, the largest and most diverse biological org…
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Foundation models trained at scale exhibit remarkable emergent behaviors, learning new capabilities beyond their initial training objectives. We find such emergent behaviors in biological vision models via large-scale contrastive vision-language training. To achieve this, we first curate TreeOfLife-200M, comprising 214 million images of living organisms, the largest and most diverse biological organism image dataset to date. We then train BioCLIP 2 on TreeOfLife-200M to distinguish different species. Despite the narrow training objective, BioCLIP 2 yields extraordinary accuracy when applied to various biological visual tasks such as habitat classification and trait prediction. We identify emergent properties in the learned embedding space of BioCLIP 2. At the inter-species level, the embedding distribution of different species aligns closely with functional and ecological meanings (e.g., beak sizes and habitats). At the intra-species level, instead of being diminished, the intra-species variations (e.g., life stages and sexes) are preserved and better separated in subspaces orthogonal to inter-species distinctions. We provide formal proof and analyses to explain why hierarchical supervision and contrastive objectives encourage these emergent properties. Crucially, our results reveal that these properties become increasingly significant with larger-scale training data, leading to a biologically meaningful embedding space.
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Submitted 23 October, 2025; v1 submitted 29 May, 2025;
originally announced May 2025.
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10 quick tips for making your software outlive your job
Authors:
Richard Littauer,
Greg Wilson,
Jan Ainali,
Eman Abdullah AlOmar,
Sylwester Arabas,
Yanina Bellini Saibene,
Kris Bubendorfer,
Kaylea Champion,
Clare Dillon,
Jouni Helske,
Pieter Huybrechts,
Daniel S. Katz,
Chang Liao,
David Lippert,
Fang Liu,
Pierre Marshall,
Daniel R. McCloy,
Ian McInerney,
Mohamed Wiem Mkaouer,
Priyanka Ojha,
Christoph Treude,
Ethan P. White
Abstract:
Loss of key personnel has always been a risk for research software projects. Key members of the team may have to step away due to illness or burnout, to care for a family member, from a loss of financial support, or because their career is going in a new direction. Today, though, political and financial changes are putting large numbers of researchers out of work simultaneously, potentially leavin…
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Loss of key personnel has always been a risk for research software projects. Key members of the team may have to step away due to illness or burnout, to care for a family member, from a loss of financial support, or because their career is going in a new direction. Today, though, political and financial changes are putting large numbers of researchers out of work simultaneously, potentially leaving large amounts of research software abandoned. This article presents ten tips to help researchers ensure that the software they have built will continue to be usable after they have left their present job -- whether in the course of voluntary career moves or researcher mobility, but particularly in cases of involuntary departure due to political or institutional changes.
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Submitted 28 October, 2025; v1 submitted 9 May, 2025;
originally announced May 2025.
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A note on the no-$(d+2)$-on-a-sphere problem
Authors:
Andrew Suk,
Ethan Patrick White
Abstract:
For fixed $d\geq 3$, we construct subsets of the $d$-dimensional lattice cube $[n]^d$ of size $n^{\frac{3}{d + 1} - o(1)}$ with no $d+2$ points on a sphere or a hyperplane. This improves the previously best known bound of $Ω(n^{\frac{1}{d-1}})$ due to Thiele from 1995.
For fixed $d\geq 3$, we construct subsets of the $d$-dimensional lattice cube $[n]^d$ of size $n^{\frac{3}{d + 1} - o(1)}$ with no $d+2$ points on a sphere or a hyperplane. This improves the previously best known bound of $Ω(n^{\frac{1}{d-1}})$ due to Thiele from 1995.
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Submitted 3 December, 2024;
originally announced December 2024.
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Leveraging tropical reef, bird and unrelated sounds for superior transfer learning in marine bioacoustics
Authors:
Ben Williams,
Bart van Merriënboer,
Vincent Dumoulin,
Jenny Hamer,
Eleni Triantafillou,
Abram B. Fleishman,
Matthew McKown,
Jill E. Munger,
Aaron N. Rice,
Ashlee Lillis,
Clemency E. White,
Catherine A. D. Hobbs,
Tries B. Razak,
Kate E. Jones,
Tom Denton
Abstract:
Machine learning has the potential to revolutionize passive acoustic monitoring (PAM) for ecological assessments. However, high annotation and compute costs limit the field's efficacy. Generalizable pretrained networks can overcome these costs, but high-quality pretraining requires vast annotated libraries, limiting its current applicability primarily to bird taxa. Here, we identify the optimum pr…
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Machine learning has the potential to revolutionize passive acoustic monitoring (PAM) for ecological assessments. However, high annotation and compute costs limit the field's efficacy. Generalizable pretrained networks can overcome these costs, but high-quality pretraining requires vast annotated libraries, limiting its current applicability primarily to bird taxa. Here, we identify the optimum pretraining strategy for a data-deficient domain using coral reef bioacoustics. We assemble ReefSet, a large annotated library of reef sounds, though modest compared to bird libraries at 2% of the sample count. Through testing few-shot transfer learning performance, we observe that pretraining on bird audio provides notably superior generalizability compared to pretraining on ReefSet or unrelated audio alone. However, our key findings show that cross-domain mixing which leverages bird, reef and unrelated audio during pretraining maximizes reef generalizability. SurfPerch, our pretrained network, provides a strong foundation for automated analysis of marine PAM data with minimal annotation and compute costs.
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Submitted 7 May, 2024; v1 submitted 25 April, 2024;
originally announced April 2024.
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Grid-drawings of graphs in three-dimensions
Authors:
Jozsef Balogh,
Ethan Patrick White
Abstract:
Using probabilistic methods, we obtain grid-drawings of graphs without crossings with low volume and small aspect ratio. We show that every $D$-degenerate graph on $n$ vertices can be drawn in $[m]^3$ where $m^3 = O(D^2 n\log n)$. In particular, every graph of bounded maximum degree can be drawn in a grid with volume $O(n \log n)$.
Using probabilistic methods, we obtain grid-drawings of graphs without crossings with low volume and small aspect ratio. We show that every $D$-degenerate graph on $n$ vertices can be drawn in $[m]^3$ where $m^3 = O(D^2 n\log n)$. In particular, every graph of bounded maximum degree can be drawn in a grid with volume $O(n \log n)$.
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Submitted 14 June, 2024; v1 submitted 2 April, 2024;
originally announced April 2024.
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Unmasking the Nuances of Loneliness: Using Digital Biomarkers to Understand Social and Emotional Loneliness in College Students
Authors:
Malik Muhammad Qirtas,
Evi Zafeirid,
Dirk Pesch,
Eleanor Bantry White
Abstract:
Background: Loneliness among students is increasing across the world, with potential consequences for mental health and academic success. To address this growing problem, accurate methods of detection are needed to identify loneliness and to differentiate social and emotional loneliness so that intervention can be personalized to individual need. Passive sensing technology provides a unique techni…
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Background: Loneliness among students is increasing across the world, with potential consequences for mental health and academic success. To address this growing problem, accurate methods of detection are needed to identify loneliness and to differentiate social and emotional loneliness so that intervention can be personalized to individual need. Passive sensing technology provides a unique technique to capture behavioral patterns linked with distinct loneliness forms, allowing for more nuanced understanding and interventions for loneliness.
Methods: To differentiate between social and emotional loneliness using digital biomarkers, our study included statistical tests, machine learning for predictive modeling, and SHAP values for feature importance analysis, revealing important factors in loneliness classification.
Results: Our analysis revealed significant behavioral differences between socially and emotionally lonely groups, particularly in terms of phone usage and location-based features , with machine learning models demonstrating substantial predictive power in classifying loneliness levels. The XGBoost model, in particular, showed high accuracy and was effective in identifying key digital biomarkers, including phone usage duration and location-based features, as significant predictors of loneliness categories.
Conclusion: This study underscores the potential of passive sensing data, combined with machine learning techniques, to provide insights into the behavioral manifestations of social and emotional loneliness among students. The identification of key digital biomarkers paves the way for targeted interventions aimed at mitigating loneliness in this population.
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Submitted 2 April, 2024;
originally announced April 2024.
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Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context
Authors:
Gemini Team,
Petko Georgiev,
Ving Ian Lei,
Ryan Burnell,
Libin Bai,
Anmol Gulati,
Garrett Tanzer,
Damien Vincent,
Zhufeng Pan,
Shibo Wang,
Soroosh Mariooryad,
Yifan Ding,
Xinyang Geng,
Fred Alcober,
Roy Frostig,
Mark Omernick,
Lexi Walker,
Cosmin Paduraru,
Christina Sorokin,
Andrea Tacchetti,
Colin Gaffney,
Samira Daruki,
Olcan Sercinoglu,
Zach Gleicher,
Juliette Love
, et al. (1112 additional authors not shown)
Abstract:
In this report, we introduce the Gemini 1.5 family of models, representing the next generation of highly compute-efficient multimodal models capable of recalling and reasoning over fine-grained information from millions of tokens of context, including multiple long documents and hours of video and audio. The family includes two new models: (1) an updated Gemini 1.5 Pro, which exceeds the February…
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In this report, we introduce the Gemini 1.5 family of models, representing the next generation of highly compute-efficient multimodal models capable of recalling and reasoning over fine-grained information from millions of tokens of context, including multiple long documents and hours of video and audio. The family includes two new models: (1) an updated Gemini 1.5 Pro, which exceeds the February version on the great majority of capabilities and benchmarks; (2) Gemini 1.5 Flash, a more lightweight variant designed for efficiency with minimal regression in quality. Gemini 1.5 models achieve near-perfect recall on long-context retrieval tasks across modalities, improve the state-of-the-art in long-document QA, long-video QA and long-context ASR, and match or surpass Gemini 1.0 Ultra's state-of-the-art performance across a broad set of benchmarks. Studying the limits of Gemini 1.5's long-context ability, we find continued improvement in next-token prediction and near-perfect retrieval (>99%) up to at least 10M tokens, a generational leap over existing models such as Claude 3.0 (200k) and GPT-4 Turbo (128k). Finally, we highlight real-world use cases, such as Gemini 1.5 collaborating with professionals on completing their tasks achieving 26 to 75% time savings across 10 different job categories, as well as surprising new capabilities of large language models at the frontier; when given a grammar manual for Kalamang, a language with fewer than 200 speakers worldwide, the model learns to translate English to Kalamang at a similar level to a person who learned from the same content.
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Submitted 16 December, 2024; v1 submitted 8 March, 2024;
originally announced March 2024.
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Evolving AI for Wellness: Dynamic and Personalized Real-time Loneliness Detection Using Passive Sensing
Authors:
Malik Muhammad Qirtas,
Evi Zafeiridi,
Eleanor Bantry White,
Dirk Pesch
Abstract:
Loneliness is a growing health concern as it can lead to depression and other associated mental health problems for people who experience feelings of loneliness over prolonged periods of time. Utilizing passive sensing methods that use smartphone and wearable sensor data to capture daily behavioural patterns offers a promising approach for the early detection of loneliness. Given the subjective na…
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Loneliness is a growing health concern as it can lead to depression and other associated mental health problems for people who experience feelings of loneliness over prolonged periods of time. Utilizing passive sensing methods that use smartphone and wearable sensor data to capture daily behavioural patterns offers a promising approach for the early detection of loneliness. Given the subjective nature of loneliness and people's varying daily routines, past detection approaches using machine learning models often face challenges with effectively detecting loneliness. This paper proposes a methodologically novel approach, particularly developing a loneliness detection system that evolves over time, adapts to new data, and provides real-time detection. Our study utilized the Globem dataset, a comprehensive collection of passive sensing data acquired over 10 weeks from university students. The base of our approach is the continuous identification and refinement of similar behavioural groups among students using an incremental clustering method. As we add new data, the model improves based on changing behavioural patterns. Parallel to this, we create and update classification models to detect loneliness among the evolving behavioural groups of students. When unique behavioural patterns are observed among student data, specialized classification models have been created. For predictions of loneliness, a collaborative effort between the generalized and specialized models is employed, treating each prediction as a vote. This study's findings reveal that group-based loneliness detection models exhibit superior performance compared to generic models, underscoring the necessity for more personalized approaches tailored to specific behavioural patterns. These results pave the way for future research, emphasizing the development of finely-tuned, individualized mental health interventions.
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Submitted 8 February, 2024;
originally announced February 2024.
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Enhancing predictive capabilities in fusion burning plasmas through surrogate-based optimization in core transport solvers
Authors:
P. Rodriguez-Fernandez,
N. T. Howard,
A. Saltzman,
S. Kantamneni,
J. Candy,
C. Holland,
M. Balandat,
S. Ament,
A. E. White
Abstract:
This work presents the PORTALS framework, which leverages surrogate modeling and optimization techniques to enable the prediction of core plasma profiles and performance with nonlinear gyrokinetic simulations at significantly reduced cost, with no loss of accuracy. The efficiency of PORTALS is benchmarked against standard methods, and its full potential is demonstrated on a unique, simultaneous 5-…
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This work presents the PORTALS framework, which leverages surrogate modeling and optimization techniques to enable the prediction of core plasma profiles and performance with nonlinear gyrokinetic simulations at significantly reduced cost, with no loss of accuracy. The efficiency of PORTALS is benchmarked against standard methods, and its full potential is demonstrated on a unique, simultaneous 5-channel (electron temperature, ion temperature, electron density, impurity density and angular rotation) prediction of steady-state profiles in a DIII-D ITER Similar Shape plasma with GPU-accelerated, nonlinear CGYRO. This paper also provides general guidelines for accurate performance predictions in burning plasmas and the impact of transport modeling in fusion pilot plants studies.
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Submitted 9 April, 2024; v1 submitted 19 December, 2023;
originally announced December 2023.
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Gemini: A Family of Highly Capable Multimodal Models
Authors:
Gemini Team,
Rohan Anil,
Sebastian Borgeaud,
Jean-Baptiste Alayrac,
Jiahui Yu,
Radu Soricut,
Johan Schalkwyk,
Andrew M. Dai,
Anja Hauth,
Katie Millican,
David Silver,
Melvin Johnson,
Ioannis Antonoglou,
Julian Schrittwieser,
Amelia Glaese,
Jilin Chen,
Emily Pitler,
Timothy Lillicrap,
Angeliki Lazaridou,
Orhan Firat,
James Molloy,
Michael Isard,
Paul R. Barham,
Tom Hennigan,
Benjamin Lee
, et al. (1326 additional authors not shown)
Abstract:
This report introduces a new family of multimodal models, Gemini, that exhibit remarkable capabilities across image, audio, video, and text understanding. The Gemini family consists of Ultra, Pro, and Nano sizes, suitable for applications ranging from complex reasoning tasks to on-device memory-constrained use-cases. Evaluation on a broad range of benchmarks shows that our most-capable Gemini Ultr…
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This report introduces a new family of multimodal models, Gemini, that exhibit remarkable capabilities across image, audio, video, and text understanding. The Gemini family consists of Ultra, Pro, and Nano sizes, suitable for applications ranging from complex reasoning tasks to on-device memory-constrained use-cases. Evaluation on a broad range of benchmarks shows that our most-capable Gemini Ultra model advances the state of the art in 30 of 32 of these benchmarks - notably being the first model to achieve human-expert performance on the well-studied exam benchmark MMLU, and improving the state of the art in every one of the 20 multimodal benchmarks we examined. We believe that the new capabilities of the Gemini family in cross-modal reasoning and language understanding will enable a wide variety of use cases. We discuss our approach toward post-training and deploying Gemini models responsibly to users through services including Gemini, Gemini Advanced, Google AI Studio, and Cloud Vertex AI.
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Submitted 9 May, 2025; v1 submitted 18 December, 2023;
originally announced December 2023.
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Improving Uniquely Decodable Codes in Binary Adder Channels
Authors:
József Balogh,
The Nguyen,
Patric R. J. Ostergard,
Ethan Patrick White,
Michael Wigal
Abstract:
We present a general method to modify existing uniquely decodable codes in the $T$-user binary adder channel. If at least one of the original constituent codes does not have average weight exactly half of the dimension, then our method produces a new set of constituent codes in a higher dimension, with a strictly higher rate. Using our method we improve the highest known rate for the $T$-user bina…
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We present a general method to modify existing uniquely decodable codes in the $T$-user binary adder channel. If at least one of the original constituent codes does not have average weight exactly half of the dimension, then our method produces a new set of constituent codes in a higher dimension, with a strictly higher rate. Using our method we improve the highest known rate for the $T$-user binary adder channel for all $T \geq 2$. This information theory problem is equivalent to co-Sidon problems initiated by Lindstr{ö}m in the 1960s, and also the multi-set union-free problem. Our results improve the known lower bounds in these settings as well.
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Submitted 10 November, 2025; v1 submitted 18 December, 2023;
originally announced December 2023.
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On Axial Symmetry in Convex Bodies
Authors:
Ritesh Goenka,
Kenneth Moore,
Wen Rui Sun,
Ethan Patrick White
Abstract:
For a two-dimensional convex body, the Kovner-Besicovitch measure of symmetry is defined as the volume ratio of the largest centrally symmetric body contained inside the body to the original body. A classical result states that the Kovner-Besicovitch measure is at least $2/3$ for every convex body and equals $2/3$ for triangles. Lassak showed that an alternative measure of symmetry, i.e., symmetry…
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For a two-dimensional convex body, the Kovner-Besicovitch measure of symmetry is defined as the volume ratio of the largest centrally symmetric body contained inside the body to the original body. A classical result states that the Kovner-Besicovitch measure is at least $2/3$ for every convex body and equals $2/3$ for triangles. Lassak showed that an alternative measure of symmetry, i.e., symmetry about a line (axiality) has a value of at least $2/3$ for every convex body. However, the smallest known value of the axiality of a convex body is around $0.81584$, achieved by a convex quadrilateral. We show that every plane convex body has axiality at least $\frac{2}{41}(10 + 3 \sqrt{2}) \approx 0.69476$, thereby establishing a separation with the central symmetry measure. Moreover, we find a family of convex quadrilaterals with axiality approaching $\frac{1}{3}(\sqrt{2}+1) \approx 0.80474$. We also establish improved bounds for a ``folding" measure of axial symmetry for plane convex bodies. Finally, we establish improved bounds for a generalization of axiality to high-dimensional convex bodies.
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Submitted 21 September, 2023;
originally announced September 2023.
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The Relationship between Loneliness and Depression among College Students: Mining data derived from Passive Sensing
Authors:
Malik Muhammad Qirtas,
Evi Zafeiridi,
Eleanor Bantry White,
Dirk Pesch
Abstract:
Loneliness and depression are interrelated mental health issues affecting students well-being. Using passive sensing data provides a novel approach to examine the granular behavioural indicators differentiating loneliness and depression, and the mediators in their relationship. This study aimed to investigate associations between behavioural features and loneliness and depression among students, e…
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Loneliness and depression are interrelated mental health issues affecting students well-being. Using passive sensing data provides a novel approach to examine the granular behavioural indicators differentiating loneliness and depression, and the mediators in their relationship. This study aimed to investigate associations between behavioural features and loneliness and depression among students, exploring the complex relationships between these mental health conditions and associated behaviours. This study combined regression analysis, mediation analysis, and machine learning analysis to explore relationships between behavioural features, loneliness, and depression using passive sensing data, capturing daily life behaviours such as physical activity, phone usage, sleep patterns, and social interactions. Results revealed significant associations between behavioural features and loneliness and depression, emphasizing their interconnected nature. Increased activity and sleep duration were identified as protective factors. Distinct behavioural features for each condition were also found. Mediation analysis highlighted significant indirect effects in the relationship between loneliness and depression. The XGBoost model achieved the highest accuracy in predicting these conditions. This study demonstrated the importance of using passive sensing data and a multi-method approach to understand the complex relationship between loneliness, depression, and associated behaviours. Identifying specific behavioural features and mediators contributes to a deeper understanding of factors influencing loneliness and depression among students. This comprehensive perspective emphasizes the importance of interdisciplinary collaboration for a more nuanced understanding of complex human experiences.
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Submitted 29 August, 2023;
originally announced August 2023.
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Improved estimates on the number of unit perimeter triangles
Authors:
Ritesh Goenka,
Kenneth Moore,
Ethan Patrick White
Abstract:
We obtain new upper and lower bounds on the number of unit perimeter triangles spanned by points in the plane. We also establish improved bounds in the special case where the point set is a section of the integer grid.
We obtain new upper and lower bounds on the number of unit perimeter triangles spanned by points in the plane. We also establish improved bounds in the special case where the point set is a section of the integer grid.
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Submitted 8 April, 2023;
originally announced April 2023.
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Plastic Contaminant Detection in Aerial Imagery of Cotton Fields with Deep Learning
Authors:
Pappu Kumar Yadav,
J. Alex Thomasson,
Robert G. Hardin,
Stephen W. Searcy,
Ulisses Braga-Neto,
Sorin C. Popescu,
Roberto Rodriguez,
Daniel E Martin,
Juan Enciso,
Karem Meza,
Emma L. White
Abstract:
Plastic shopping bags that get carried away from the side of roads and tangled on cotton plants can end up at cotton gins if not removed before the harvest. Such bags may not only cause problem in the ginning process but might also get embodied in cotton fibers reducing its quality and marketable value. Therefore, it is required to detect, locate, and remove the bags before cotton is harvested. Ma…
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Plastic shopping bags that get carried away from the side of roads and tangled on cotton plants can end up at cotton gins if not removed before the harvest. Such bags may not only cause problem in the ginning process but might also get embodied in cotton fibers reducing its quality and marketable value. Therefore, it is required to detect, locate, and remove the bags before cotton is harvested. Manually detecting and locating these bags in cotton fields is labor intensive, time-consuming and a costly process. To solve these challenges, we present application of four variants of YOLOv5 (YOLOv5s, YOLOv5m, YOLOv5l and YOLOv5x) for detecting plastic shopping bags using Unmanned Aircraft Systems (UAS)-acquired RGB (Red, Green, and Blue) images. We also show fixed effect model tests of color of plastic bags as well as YOLOv5-variant on average precision (AP), mean average precision (mAP@50) and accuracy. In addition, we also demonstrate the effect of height of plastic bags on the detection accuracy. It was found that color of bags had significant effect (p < 0.001) on accuracy across all the four variants while it did not show any significant effect on the AP with YOLOv5m (p = 0.10) and YOLOv5x (p = 0.35) at 95% confidence level. Similarly, YOLOv5-variant did not show any significant effect on the AP (p = 0.11) and accuracy (p = 0.73) of white bags, but it had significant effects on the AP (p = 0.03) and accuracy (p = 0.02) of brown bags including on the mAP@50 (p = 0.01) and inference speed (p < 0.0001). Additionally, height of plastic bags had significant effect (p < 0.0001) on overall detection accuracy. The findings reported in this paper can be useful in speeding up removal of plastic bags from cotton fields before harvest and thereby reducing the amount of contaminants that end up at cotton gins.
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Submitted 14 December, 2022;
originally announced December 2022.
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RandCrowns: A Quantitative Metric for Imprecisely Labeled Tree Crown Delineation
Authors:
Dylan Stewart,
Alina Zare,
Sergio Marconi,
Ben G. Weinstein,
Ethan P. White,
Sarah J. Graves,
Stephanie A. Bohlman,
Aditya Singh
Abstract:
Supervised methods for object delineation in remote sensing require labeled ground-truth data. Gathering sufficient high quality ground-truth data is difficult, especially when targets are of irregular shape or difficult to distinguish from background or neighboring objects. Tree crown delineation provides key information from remote sensing images for forestry, ecology, and management. However, t…
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Supervised methods for object delineation in remote sensing require labeled ground-truth data. Gathering sufficient high quality ground-truth data is difficult, especially when targets are of irregular shape or difficult to distinguish from background or neighboring objects. Tree crown delineation provides key information from remote sensing images for forestry, ecology, and management. However, tree crowns in remote sensing imagery are often difficult to label and annotate due to irregular shape, overlapping canopies, shadowing, and indistinct edges. There are also multiple approaches to annotation in this field (e.g., rectangular boxes vs. convex polygons) that further contribute to annotation imprecision. However, current evaluation methods do not account for this uncertainty in annotations, and quantitative metrics for evaluation can vary across multiple annotators. In this paper, we address these limitations by developing an adaptation of the Rand index for weakly-labeled crown delineation that we call RandCrowns. Our new RandCrowns evaluation metric provides a method to appropriately evaluate delineated tree crowns while taking into account imprecision in the ground-truth delineations. The RandCrowns metric reformulates the Rand index by adjusting the areas over which each term of the index is computed to account for uncertain and imprecise object delineation labels. Quantitative comparisons to the commonly used intersection over union method shows a decrease in the variance generated by differences among multiple annotators. Combined with qualitative examples, our results suggest that the RandCrowns metric is more robust for scoring target delineations in the presence of uncertainty and imprecision in annotations that are inherent to tree crown delineation.
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Submitted 20 October, 2021; v1 submitted 5 May, 2021;
originally announced May 2021.
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Combinatorics of intervals in the plane I: trapezoids
Authors:
Daniel Di Benedetto,
Jozsef Solymosi,
Ethan White
Abstract:
We study arrangements of intervals in $\mathbb{R}^2$ for which many pairs form trapezoids. We show that any set of intervals forming many trapezoids must have underlying algebraic structure, which we characterise. This leads to some unexpected examples of sets of intervals forming many trapezoids, where an important role is played by degree 2 curves.
We study arrangements of intervals in $\mathbb{R}^2$ for which many pairs form trapezoids. We show that any set of intervals forming many trapezoids must have underlying algebraic structure, which we characterise. This leads to some unexpected examples of sets of intervals forming many trapezoids, where an important role is played by degree 2 curves.
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Submitted 18 May, 2020;
originally announced May 2020.
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Recurrent U-net: Deep learning to predict daily summertime ozone in the United States
Authors:
Tai-Long He,
Dylan B. A. Jones,
Binxuan Huang,
Yuyang Liu,
Kazuyuki Miyazaki,
Zhe Jiang,
E. Charlie White,
Helen M. Worden,
John R. Worden
Abstract:
We use a hybrid deep learning model to predict June-July-August (JJA) daily maximum 8-h average (MDA8) surface ozone concentrations in the US. A set of meteorological fields from the ERA-Interim reanalysis as well as monthly mean NO$_x$ emissions from the Community Emissions Data System (CEDS) inventory are selected as predictors. Ozone measurements from the US Environmental Protection Agency (EPA…
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We use a hybrid deep learning model to predict June-July-August (JJA) daily maximum 8-h average (MDA8) surface ozone concentrations in the US. A set of meteorological fields from the ERA-Interim reanalysis as well as monthly mean NO$_x$ emissions from the Community Emissions Data System (CEDS) inventory are selected as predictors. Ozone measurements from the US Environmental Protection Agency (EPA) Air Quality System (AQS) from 1980 to 2009 are used to train the model, whereas data from 2010 to 2014 are used to evaluate the performance of the model. The model captures well daily, seasonal and interannual variability in MDA8 ozone across the US. Feature maps show that the model captures teleconnections between MDA8 ozone and the meteorological fields, which are responsible for driving the ozone dynamics. We used the model to evaluate recent trends in NO$_x$ emissions in the US and found that the trend in the EPA emission inventory produced the largest negative bias in MDA8 ozone between 2010-2016. The top-down emission trends from the Tropospheric Chemistry Reanalysis (TCR-2), which is based on satellite observations, produced predictions in best agreement with observations. In urban regions, the trend in AQS NO$_2$ observations provided ozone predictions in agreement with observations, whereas in rural regions the satellite-derived trends produced the best agreement. In both rural and urban regions the EPA trend resulted in the largest negative bias in predicted ozone. Our results suggest that the EPA inventory is overestimating the reductions in NO$_x$ emissions and that the satellite-derived trend reflects the influence of reductions in NO$_x$ emissions as well as changes in background NO$_x$. Our results demonstrate the significantly greater predictive capability that the deep learning model provides over conventional atmospheric chemical transport models for air quality analyses.
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Submitted 16 August, 2019;
originally announced August 2019.
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Best Practices for Scientific Computing
Authors:
Greg Wilson,
D. A. Aruliah,
C. Titus Brown,
Neil P. Chue Hong,
Matt Davis,
Richard T. Guy,
Steven H. D. Haddock,
Katy Huff,
Ian M. Mitchell,
Mark Plumbley,
Ben Waugh,
Ethan P. White,
Paul Wilson
Abstract:
Scientists spend an increasing amount of time building and using software. However, most scientists are never taught how to do this efficiently. As a result, many are unaware of tools and practices that would allow them to write more reliable and maintainable code with less effort. We describe a set of best practices for scientific software development that have solid foundations in research and e…
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Scientists spend an increasing amount of time building and using software. However, most scientists are never taught how to do this efficiently. As a result, many are unaware of tools and practices that would allow them to write more reliable and maintainable code with less effort. We describe a set of best practices for scientific software development that have solid foundations in research and experience, and that improve scientists' productivity and the reliability of their software.
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Submitted 26 September, 2013; v1 submitted 30 September, 2012;
originally announced October 2012.
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Clustering for Improved Learning in Maze Traversal Problem
Authors:
Eddie White
Abstract:
The maze traversal problem (finding the shortest distance to the goal from any position in a maze) has been an interesting challenge in computational intelligence. Recent work has shown that the cellular simultaneous recurrent neural network (CSRN) can solve this problem for simple mazes. This thesis focuses on exploiting relevant information about the maze to improve learning and decrease the t…
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The maze traversal problem (finding the shortest distance to the goal from any position in a maze) has been an interesting challenge in computational intelligence. Recent work has shown that the cellular simultaneous recurrent neural network (CSRN) can solve this problem for simple mazes. This thesis focuses on exploiting relevant information about the maze to improve learning and decrease the training time for the CSRN to solve mazes. Appropriate variables are identified to create useful clusters using relevant information. The CSRN was next modified to allow for an additional external input. With this additional input, several methods were tested and results show that clustering the mazes improves the overall learning of the traversal problem for the CSRN.
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Submitted 6 August, 2009;
originally announced August 2009.