pwc-archive/papers-with-abstracts huggingface dataset:
From Artificial Intelligence to Active Inference: The Key to True AI and 6G World Brain [Invited]
Authors: Martin Maier
Date: 2025-04-29 00:00:00

In his opening OFC plenary talk back in 2021, Alibaba Group's Yiqun Cai notably added in the follow-up Q&A that today's complex networks are more than computer science - they grow, they are life. This entails that future networks may be better viewed as techno-social systems that resemble biological superorganisms with brain-like cognitive capabilities. Fast-forwarding, there is now growing awareness that we have to completely change our networks from being static into being a living entity that would act as an AI-powered network `brain', as recently stated by Bruno Zerbib, Chief Technology and Innovation Officer of France's Orange, at the Mobile World Congress (MWC) 2025. Even though AI was front and center at both MWC and OFC 2025 and has been widely studied in the context of optical networks, there are currently no publications on active inference in optical (and less so mobile) networks available. Active inference is an ideal methodology for developing more advanced AI systems by biomimicking the way living intelligent systems work, while overcoming the limitations of today's AI related to training, learning, and explainability. Active inference is considered the key to true AI: Less artificial, more intelligent. The goal of this paper is twofold. First, we aim at enabling optical network researchers to conceptualize new research lines for future optical networks with human-AI interaction capabilities by introducing them to the main mathematical concepts of the active inference framework. Second, we demonstrate how to move AI research beyond the human brain toward the 6G world brain by exploring the role of mycorrhizal networks, the largest living organism on planet Earth, in the AI vision and R&D roadmap for the next decade and beyond laid out by Karl Friston, the father of active inference.
Synergistic interplay of morphology and metabolic activity rule response to CAR-T cells in B-cell lymphomas
Authors: Yifan Chen, Soukaina Sabir, Christina Kuttler, Juan Belmonte-Beitia, Alvaro Mártínez-Rubio, Lourdes Martín-Martín, Lucía López-Corral, Alejandro Martín-Sancho, J. Cristobal Cañadas Salazar, Carlos Montes-Fuentes, M. Pilar Tamayo-Alonso, Angel Cedillo, Pascual Balsalobre, Pere Barba, Antonio Pérez-Martínez, Víctor M. Pérez-García
Date: 2025-05-17 00:00:00

Cellular immunotherapies are one of the mainstream cancer treatments unveiling the power of the patient's immune system to fight tumors. CAR T-cell therapy, based on genetically engineered T cells, has demonstrated significant potential in treating hematological malignancies, including B-cell lymphomas. This treatment has complex longitudinal dynamics due to the interplay of different T-cell phenotypes (e.g. effector and memory), the expansion of the drug and the cytotoxic effect on both normal and cancerous B-cells, the exhaustion of the immune cells, the tumor immunosupressive environments, and more. Thus, the outcome of the therapy is not yet well understood leading to a variety of responses ranging from sustained complete responses, different types of partial responses, or no response at all. We developed a mechanistic model for the interaction between CAR T- and cancerous B-cells, accounting for the role of the tumor morphology and metabolic status. The simulations showed that lesions with irregular shapes and high proliferation could contribute to long term progression by potentially increasing their immunosuppressive capabilities impairing CAR T-cell efficacy. We analyzed 18F-FDG PET/CT imaging data from 63 relapsed/refractory diffuse large B-cell lymphoma receiving CAR T-cells, quantifying radiomic features including tumor sphericity and lesion aggressiveness through standardized uptake values (SUV). Statistical analyses revealed significant correlations between these metrics and progression-free survival (PFS), emphasizing that individual lesions with complex morphology and elevated metabolism play a critical role in shaping long-term treatment outcomes. We demonstrated the potential of using data-driven mathematical models in finding molecular-imaging based biomarkers to identify lymphoma patients treated with CAR T-cell therapy having higher risk of disease progression.
Virtual Cells: Predict, Explain, Discover
Authors: Emmanuel Noutahi, Jason Hartford, Prudencio Tossou, Shawn Whitfield, Alisandra K. Denton, Cas Wognum, Kristina Ulicna, Michael Craig, Jonathan Hsu, Michael Cuccarese, Emmanuel Bengio, Dominique Beaini, Christopher Gibson, Daniel Cohen, Berton Earnshaw
Date: 2025-05-20 00:00:00
Tasks: Drug Discovery

Drug discovery is fundamentally a process of inferring the effects of treatments on patients, and would therefore benefit immensely from computational models that can reliably simulate patient responses, enabling researchers to generate and test large numbers of therapeutic hypotheses safely and economically before initiating costly clinical trials. Even a more specific model that predicts the functional response of cells to a wide range of perturbations would be tremendously valuable for discovering safe and effective treatments that successfully translate to the clinic. Creating such virtual cells has long been a goal of the computational research community that unfortunately remains unachieved given the daunting complexity and scale of cellular biology. Nevertheless, recent advances in AI, computing power, lab automation, and high-throughput cellular profiling provide new opportunities for reaching this goal. In this perspective, we present a vision for developing and evaluating virtual cells that builds on our experience at Recursion. We argue that in order to be a useful tool to discover novel biology, virtual cells must accurately predict the functional response of a cell to perturbations and explain how the predicted response is a consequence of modifications to key biomolecular interactions. We then introduce key principles for designing therapeutically-relevant virtual cells, describe a lab-in-the-loop approach for generating novel insights with them, and advocate for biologically-grounded benchmarks to guide virtual cell development. Finally, we make the case that our approach to virtual cells provides a useful framework for building other models at higher levels of organization, including virtual patients. We hope that these directions prove useful to the research community in developing virtual models optimized for positive impact on drug discovery outcomes.
Compositional amortized inference for large-scale hierarchical Bayesian models
Authors: Jonas Arruda, Vikas Pandey, Catherine Sherry, Margarida Barroso, Xavier Intes, Jan Hasenauer, Stefan T. Radev
Date: 2025-05-20 00:00:00
Tasks: Bayesian Inference

Amortized Bayesian inference (ABI) has emerged as a powerful simulation-based approach for estimating complex mechanistic models, offering fast posterior sampling via generative neural networks. However, extending ABI to hierarchical models, a cornerstone of modern Bayesian analysis, remains a major challenge due to the difficulty of scaling to large numbers of parameters. In this work, we build on compositional score matching (CSM), a divide-and-conquer strategy for Bayesian updating using diffusion models. To address existing stability issues of CSM, we propose adaptive solvers coupled with a novel, error-damping compositional estimator. Our proposed method remains stable even with hundreds of thousands of data points and parameters. We validate our approach on a controlled toy example, a high-dimensional spatial autoregressive model, and a real-world advanced microscopy biological application task involving over 750,000 parameters.
BioCube: A Multimodal Dataset for Biodiversity Research
Authors: Stylianos Stasinos, Martino Mensio, Elena Lazovik, Athanasios Trantas
Date: 2025-05-16 00:00:00

Biodiversity research requires complete and detailed information to study ecosystem dynamics at different scales. Employing data-driven methods like Machine Learning is getting traction in ecology and more specific biodiversity, offering alternative modelling pathways. For these methods to deliver accurate results there is the need for large, curated and multimodal datasets that offer granular spatial and temporal resolutions. In this work, we introduce BioCube, a multimodal, fine-grained global dataset for ecology and biodiversity research. BioCube incorporates species observations through images, audio recordings and descriptions, environmental DNA, vegetation indices, agricultural, forest, land indicators, and high-resolution climate variables. All observations are geospatially aligned under the WGS84 geodetic system, spanning from 2000 to 2020. The dataset will become available at https://huggingface.co/datasets/BioDT/BioCube while the acquisition and processing code base at https://github.com/BioDT/bfm-data.
Modeling the impact of control zone restrictions on pig placement in simulated African swine fever in the United States
Authors: Chunlin Yi, Jason A. Galvis, Gustavo Machado
Date: 2025-05-20 00:00:00

African swine fever (ASF) is a highly contagious viral disease that poses a significant threat to the swine industry, requiring stringent control measures, including movement restrictions that delay pig placements, impacting business continuity. The number and economic impact of unplaced healthy animals due to control zone restrictions remains unmeasured. This study evaluates the economic and epidemiological impacts of control zone placement restrictions during simulated ASF outbreaks in U.S. commercial swine farms. We model the spread of ASF and apply the U.S. National Response Plan (NRP) alongside alternative mitigation strategies, analyzing key metrics such as the number of unplaced pigs, depopulated pigs, infected farms, and total economic losses. Our findings estimate the median number of unplaced pigs in the first year was 153,020 (IQR 27,377 to 1,307,899) under the NRP scenario. Shorter control zone durations (20 to 25 days) effectively reduce the median number of unplaced pigs by 16.7% to 33.5%, whereas longer durations (40 days) increase unplacement numbers by 32%. The median number of depopulated pigs remains broadly consistent across all durations. Expanding the infected zone (5 to 15 km) increases the median number of unplaced pigs by 53.6% to 282% while reducing depopulated pigs by 28.8% to 73.9%, respectively. Economic losses are estimated through a model that includes depopulated and unplaced animals requiring culling. We examined the situations when 5%, 12%, or 20% of unplaced pigs required culling and found that the total cost ranged from zero (no second infection) to over $800 million.
Physiological and Transcriptional Responses of Arabidopsis thaliana to Simulated Lunar and Martian Regolith Substrates
Authors: A'nya Buckner, Sarah Lang, Rafael Loureiro
Date: 2025-05-19 00:00:00

The integration of plant-based bioregenerative life support systems is a central objective in NASA's Moon to Mars strategy. Arabidopsis thaliana, a model organism with extensive genomic resources, serves as a key species to investigate plant resilience in extraterrestrial environments. We assessed the physiological and gene expression responses of A. thaliana (Col-0) grown in two off-world regolith simulants: LHS-2 (lunar highlands) and MGS-1 (Martian global). Plants exposed to these substrates exhibited significant reductions in root elongation, biomass, and chlorophyll content, along with elevated anthocyanin levels and transcriptional upregulation of stress-related genes including IRT1, PCS1, SOD1, and JAZ1. Evidence of jasmonic acid pathway activation and auxin signaling suppression suggests metal-induced hormonal misregulation. Our integrated analysis of morphological traits, pigment accumulation, and transcriptomic profiles reveals distinct mineral-specific stress responses, offering critical insights into substrate engineering strategies for future space agriculture.
InterFeat: An Automated Pipeline for Finding Interesting Hypotheses in Structured Biomedical Data
Authors: Dan Ofer, Michal Linial, Dafna Shahaf
Date: 2025-05-18 00:00:00
Tasks: Knowledge Graphs, scientific discovery

Finding interesting phenomena is the core of scientific discovery, but it is a manual, ill-defined concept. We present an integrative pipeline for automating the discovery of interesting simple hypotheses (feature-target relations with effect direction and a potential underlying mechanism) in structured biomedical data. The pipeline combines machine learning, knowledge graphs, literature search and Large Language Models. We formalize "interestingness" as a combination of novelty, utility and plausibility. On 8 major diseases from the UK Biobank, our pipeline consistently recovers risk factors years before their appearance in the literature. 40--53% of our top candidates were validated as interesting, compared to 0--7% for a SHAP-based baseline. Overall, 28% of 109 candidates were interesting to medical experts. The pipeline addresses the challenge of operationalizing "interestingness" scalably and for any target. We release data and code: https://github.com/LinialLab/InterFeat
Mechanistic inference of stochastic gene expression from structured single-cell data
Authors: Christopher E. Miles
Date: 2025-05-16 00:00:00

Single-cell gene expression measurements encode variability spanning molecular noise, cellular heterogeneity, and technical artifacts. Mechanistic models provide a principled framework to disentangle these sources and extract insight, but inferring underlying dynamics from standard sequencing count data faces fundamental limitations. Structured datasets with temporal, spatial, or multimodal features offer constraints that help resolve these ambiguities, but demand more complex models and advanced inference strategies, including machine learning techniques with associated tradeoffs. This review highlights recent progress in the judicious integration of structured single-cell data, stochastic model development, and innovative inference strategies to extract gene-level insights. These approaches lay the foundation for mechanistic understanding of regulatory networks and multicellular systems.
MOSAIK: Multi-Origin Spatial Transcriptomics Analysis and Integration Kit
Authors: Anthony Baptista, Rosamond Nuamah, Ciro Chiappini, Anita Grigoriadis
Date: 2025-05-16 00:00:00

Spatial transcriptomics (ST) has revolutionised transcriptomics analysis by preserving tissue architecture, allowing researchers to study gene expression in its native spatial context. However, despite its potential, ST still faces significant technical challenges. Two major issues include: (1) the integration of raw data into coherent and reproducible analysis workflows, and (2) the accurate assignment of transcripts to individual cells. To address these challenges, we present MOSAIK, the first fully integrated, end-to-end workflow that supports raw data from both NanoString CosMx Spatial Molecular Imager (CosMx) and 10x Genomics Xenium In Situ (Xenium). MOSAIK (Multi-Origin Spatial Transcriptomics Analysis and Integration Kit) unifies transcriptomics and imaging data into a single Python object based on the spatialdata format. This unified structure ensures compatibility with a broad range of Python tools, enabling robust quality control and downstream analyses. With MOSAIK, users can perform advanced analyses such as re-segmentation (to more accurately assign transcripts to individual cells), cell typing, tissue domain identification, and cell-cell communication within a seamless and reproducible Python environment.
The Stablecoin Discount: Evidence of Tether's U.S. Treasury Bill Market Share in Lowering Yields
Authors: Lennart Ante, Aman Saggu, Ingo Fiedler
Date: 2025-05-18 00:00:00
Tasks: counterfactual

Stablecoins represent a critical bridge between cryptocurrency and traditional finance, with Tether (USDT) dominating the sector as the largest stablecoin by market capitalization. By Q1 2025, Tether directly held approximately $98.5 billion in U.S. Treasury bills, representing 1.6% of all outstanding Treasury bills, making it one of the largest non-sovereign buyers in this crucial asset class, on par with nation-state-level investors. This paper investigates how Tether's market share of U.S. Treasury bills influences corresponding yields. The baseline semi-log time trend model finds that a 1% increase in Tether's market share is associated with a 1-month yield reduction of 3.8%, corresponding to 14-16 basis points. However, threshold regression analysis reveals a critical market share threshold of 0.973%, above which the yield impact intensifies significantly. In this high regime, a 1% market share increase reduces 1-month yields by 6.3%. At the end of Q1 2025, Tether's market share placed it firmly within this high-impact regime, reducing 1-month yields by around 24 basis points relative to a counterfactual. In absolute terms, Tether's demand for Treasury Bills equates to roughly $15 billion in annual interest savings for the U.S. government. Aligning with theories of liquidity saturation and nonlinear price impact, these results highlight that stablecoin demand can reduce sovereign funding costs and provide a potential buffer against market shocks.
Logarithmic resilience risk metrics that address the huge variations in blackout cost
Authors: Arslan Ahmad, Ian Dobson
Date: 2025-05-17 00:00:00

Resilience risk metrics must address the customer cost of the largest blackouts of greatest impact. However, there are huge variations in blackout cost in observed distribution utility data that make it impractical to properly estimate the mean large blackout cost and the corresponding risk. These problems are caused by the heavy tail observed in the distribution of customer costs. To solve these problems, we propose resilience metrics that describe large blackout risk using the mean of the logarithm of the cost of large-cost blackouts, the slope index of the heavy tail, and the frequency of large-cost blackouts.
Zero-Shot Forecasting Mortality Rates: A Global Study
Authors: Gabor Petnehazi, Laith Al Shaggah, Jozsef Gall, Bernadett Aradi
Date: 2025-05-17 00:00:00
Tasks: Model Selection, Time Series Forecasting

This study explores the potential of zero-shot time series forecasting, an innovative approach leveraging pre-trained foundation models, to forecast mortality rates without task-specific fine-tuning. We evaluate two state-of-the-art foundation models, TimesFM and CHRONOS, alongside traditional and machine learning-based methods across three forecasting horizons (5, 10, and 20 years) using data from 50 countries and 111 age groups. In our investigations, zero-shot models showed varying results: while CHRONOS delivered competitive shorter-term forecasts, outperforming traditional methods like ARIMA and the Lee-Carter model, TimesFM consistently underperformed. Fine-tuning CHRONOS on mortality data significantly improved long-term accuracy. A Random Forest model, trained on mortality data, achieved the best overall performance. These findings underscore the potential of zero-shot forecasting while highlighting the need for careful model selection and domain-specific adaptation.
Foundation Time-Series AI Model for Realized Volatility Forecasting
Authors: Anubha Goel, Puneet Pasricha, Martin Magris, Juho Kanniainen
Date: 2025-05-16 00:00:00
Tasks: Incremental Learning, Time Series

Time series foundation models (FMs) have emerged as a popular paradigm for zero-shot multi-domain forecasting. These models are trained on numerous diverse datasets and claim to be effective forecasters across multiple different time series domains, including financial data. In this study, we evaluate the effectiveness of FMs, specifically the TimesFM model, for volatility forecasting, a core task in financial risk management. We first evaluate TimesFM in its pretrained (zero-shot) form, followed by our custom fine-tuning procedure based on incremental learning, and compare the resulting models against standard econometric benchmarks. While the pretrained model provides a reasonable baseline, our findings show that incremental fine-tuning, which allows the model to adapt to new financial return data over time, is essential for learning volatility patterns effectively. Fine-tuned variants not only improve forecast accuracy but also statistically outperform traditional models, as demonstrated through Diebold-Mariano and Giacomini-White tests. These results highlight the potential of foundation models as scalable and adaptive tools for financial forecasting-capable of delivering strong performance in dynamic market environments when paired with targeted fine-tuning strategies.
Accuracy and Fairness of Facial Recognition Technology in Low-Quality Police Images: An Experiment With Synthetic Faces
Authors: Maria Cuellar, Hon Kiu, To, Arush Mehrotra
Date: 2025-05-20 00:00:00
Tasks: Fairness

Facial recognition technology (FRT) is increasingly used in criminal investigations, yet most evaluations of its accuracy rely on high-quality images, unlike those often encountered by law enforcement. This study examines how five common forms of image degradation--contrast, brightness, motion blur, pose shift, and resolution--affect FRT accuracy and fairness across demographic groups. Using synthetic faces generated by StyleGAN3 and labeled with FairFace, we simulate degraded images and evaluate performance using Deepface with ArcFace loss in 1:n identification tasks. We perform an experiment and find that false positive rates peak near baseline image quality, while false negatives increase as degradation intensifies--especially with blur and low resolution. Error rates are consistently higher for women and Black individuals, with Black females most affected. These disparities raise concerns about fairness and reliability when FRT is used in real-world investigative contexts. Nevertheless, even under the most challenging conditions and for the most affected subgroups, FRT accuracy remains substantially higher than that of many traditional forensic methods. This suggests that, if appropriately validated and regulated, FRT should be considered a valuable investigative tool. However, algorithmic accuracy alone is not sufficient: we must also evaluate how FRT is used in practice, including user-driven data manipulation. Such cases underscore the need for transparency and oversight in FRT deployment to ensure both fairness and forensic validity.
Hybrid Bernstein Normalizing Flows for Flexible Multivariate Density Regression with Interpretable Marginals
Authors: Marcel Arpogaus, Thomas Kneib, Thomas Nagler, David Rügamer
Date: 2025-05-20 00:00:00

Density regression models allow a comprehensive understanding of data by modeling the complete conditional probability distribution. While flexible estimation approaches such as normalizing flows (NF) work particularly well in multiple dimensions, interpreting the input-output relationship of such models is often difficult, due to the black-box character of deep learning models. In contrast, existing statistical methods for multivariate outcomes such as multivariate conditional transformation models (MCTM) are restricted in flexibility and are often not expressive enough to represent complex multivariate probability distributions. In this paper, we combine MCTM with state-of-the-art and autoregressive NF to leverage the transparency of MCTM for modeling interpretable feature effects on the marginal distributions in the first step and the flexibility of neural-network-based NF techniques to account for complex and non-linear relationships in the joint data distribution. We demonstrate our method's versatility in various numerical experiments and compare it with MCTM and other NF models on both simulated and real-world data.