Improving Patient Safety Culture in Conflict-Affected Zones: A Cross-Sectional Survey of North Kivu Surgical Personnel in the Democratic Republic of the Congo.
Jacques Fadhili Bake
Claude Kasereka Masumbuko
Zacharie Tsongo Kibendelwa
Georges Bushu Lubuto
Jean‐Claude Mafuta Kyembwa
Esaie Kasereka Nzala
Papy Waleyirwe Kakule
Clovis Bwami Akumbi
Jean Zanga Kitutu
Tresor Basubi Wakilongo
Theophile Kubuya Hangi
Wilson Katembo Kwiraviwe
Benjamin Musemakweli
Beate Tshikudju Bahati
Steve Kisembo Bakabona
Dan Poenaru
BACKGROUND Patient safety culture significantly impacts outcomes in surgery, where preventable errors can occur. This study assessed patient… (see more) safety culture and its determinants in operating rooms across North Kivu, a conflict-affected province in the eastern Democratic Republic of the Congo (DRC). METHODS A descriptive multicenter cross-sectional study was conducted from July to September 2024 in five urban and six rural hospitals. The French version of the Hospital Survey on Patient Safety Culture (HSOPSC) questionnaire was administered to 328 operating room healthcare professionals. RESULTS The response rate was 78% (256 completed surveys). Urban hospitals accounted for 55.5% of respondents, who were 73.4% male and 62.5% under the age of 40. The overall composite score for patient safety culture was 63.2%. Teamwork (81.1%) and management support for patient safety (77.7%) received the highest positive responses, whereas error reporting (39.9%) and patient safety event reporting (50%) scored lower. Half (49.6%) of the respondents rated patient safety as excellent or very good. There were no significant differences in overall mean composite scores between urban and rural hospitals (p = 0.677) and between medical and paramedical staff (p = 0.694). CONCLUSIONS The patient safety culture rating in North Kivu falls below international standards, highlighting an urgent need for improvement, particularly in error response and event reporting. Developing a tailored patient safety bundle for the region is essential to enhance overall health outcomes.
2025-02-07
World Journal of Surgery (published)
doi.org
Agency Is Frame-Dependent
David Abel
Andre Barreto
Michael Bowling
Will Dabney
Shi Dong
Steven Hansen
Anna Harutyunyan
Khimya Khetarpal
Clare Lyle
Razvan Pascanu
Georgios Piliouras
Doina Precup
Jonathan Richens
Mark Rowland
Tom Schaul
Satinder Singh
Agency is a system's capacity to steer outcomes toward a goal, and is a central topic of study across biology, philosophy, cognitive science… (see more), and artificial intelligence. Determining if a system exhibits agency is a notoriously difficult question: Dennett (1989), for instance, highlights the puzzle of determining which principles can decide whether a rock, a thermostat, or a robot each possess agency. We here address this puzzle from the viewpoint of reinforcement learning by arguing that agency is fundamentally frame-dependent: Any measurement of a system's agency must be made relative to a reference frame. We support this claim by presenting a philosophical argument that each of the essential properties of agency proposed by Barandiaran et al. (2009) and Moreno (2018) are themselves frame-dependent. We conclude that any basic science of agency requires frame-dependence, and discuss the implications of this claim for reinforcement learning.
2025-02-06
ArXiv (preprint)
doi.org
arxiv.org
Agency Is Frame-Dependent
David Abel
Andre Barreto
Michael Bowling
Will Dabney
Shi Dong
Steven Hansen
A. Harutyunyan
Khimya Khetarpal
Clare Lyle
Razvan Pascanu
Georgios Piliouras
Doina Precup
Jonathan Richens
Mark Rowland
Tom Schaul
Satinder Singh
Agency is a system's capacity to steer outcomes toward a goal, and is a central topic of study across biology, philosophy, cognitive science… (see more), and artificial intelligence. Determining if a system exhibits agency is a notoriously difficult question: Dennett (1989), for instance, highlights the puzzle of determining which principles can decide whether a rock, a thermostat, or a robot each possess agency. We here address this puzzle from the viewpoint of reinforcement learning by arguing that agency is fundamentally frame-dependent: Any measurement of a system's agency must be made relative to a reference frame. We support this claim by presenting a philosophical argument that each of the essential properties of agency proposed by Barandiaran et al. (2009) and Moreno (2018) are themselves frame-dependent. We conclude that any basic science of agency requires frame-dependence, and discuss the implications of this claim for reinforcement learning.
2025-02-06
ArXiv (preprint)
arxiv.org
arxiv.org
Conditional Diffusion Models are Medical Image Classifiers that Provide Explainability and Uncertainty for Free
Gian Mario Favero
Parham Saremi
Emily Kaczmarek
Brennan Nichyporuk
Tal Arbel
Discriminative classifiers have become a foundational tool in deep learning for medical imaging, excelling at learning separable features of… (see more) complex data distributions. However, these models often need careful design, augmentation, and training techniques to ensure safe and reliable deployment. Recently, diffusion models have become synonymous with generative modeling in 2D. These models showcase robustness across a range of tasks including natural image classification, where classification is performed by comparing reconstruction errors across images generated for each possible conditioning input. This work presents the first exploration of the potential of class conditional diffusion models for 2D medical image classification. First, we develop a novel majority voting scheme shown to improve the performance of medical diffusion classifiers. Next, extensive experiments on the CheXpert and ISIC Melanoma skin cancer datasets demonstrate that foundation and trained-from-scratch diffusion models achieve competitive performance against SOTA discriminative classifiers without the need for explicit supervision. In addition, we show that diffusion classifiers are intrinsically explainable, and can be used to quantify the uncertainty of their predictions, increasing their trustworthiness and reliability in safety-critical, clinical contexts. Further information is available on our project page: https://faverogian.github.io/med-diffusion-classifier.github.io/
2025-02-06
ArXiv (preprint)
arxiv.org
arxiv.org
FocalCodec: Low-Bitrate Speech Coding via Focal Modulation Networks
Luca Della Libera
Francesco Paissan
Cem Subakan
Mirco Ravanelli
Large language models have revolutionized natural language processing through self-supervised pretraining on massive datasets. Inspired by t… (see more)his success, researchers have explored adapting these methods to speech by discretizing continuous audio into tokens using neural audio codecs. However, existing approaches face limitations, including high bitrates, the loss of either semantic or acoustic information, and the reliance on multi-codebook designs when trying to capture both, which increases architectural complexity for downstream tasks. To address these challenges, we introduce FocalCodec, an efficient low-bitrate codec based on focal modulation that utilizes a single binary codebook to compress speech between 0.16 and 0.65 kbps. FocalCodec delivers competitive performance in speech resynthesis and voice conversion at lower bitrates than the current state-of-the-art, while effectively handling multilingual speech and noisy environments. Evaluation on downstream tasks shows that FocalCodec successfully preserves sufficient semantic and acoustic information, while also being well-suited for generative modeling. Demo samples, code and checkpoints are available at https://lucadellalib.github.io/focalcodec-web/.
2025-02-06
ArXiv (preprint)
doi.org
arxiv.org
FocalCodec: Low-Bitrate Speech Coding via Focal Modulation Networks
Luca Della Libera
Francesco Paissan
Cem Subakan
Mirco Ravanelli
Large language models have revolutionized natural language processing through self-supervised pretraining on massive datasets. Inspired by t… (see more)his success, researchers have explored adapting these methods to speech by discretizing continuous audio into tokens using neural audio codecs. However, existing approaches face limitations, including high bitrates, the loss of either semantic or acoustic information, and the reliance on multi-codebook designs when trying to capture both, which increases architectural complexity for downstream tasks. To address these challenges, we introduce FocalCodec, an efficient low-bitrate codec based on focal modulation that utilizes a single binary codebook to compress speech between 0.16 and 0.65 kbps. FocalCodec delivers competitive performance in speech resynthesis and voice conversion at lower bitrates than the current state-of-the-art, while effectively handling multilingual speech and noisy environments. Evaluation on downstream tasks shows that FocalCodec successfully preserves sufficient semantic and acoustic information, while also being well-suited for generative modeling. Demo samples, code and checkpoints are available at https://lucadellalib.github.io/focalcodec-web/.
2025-02-06
ArXiv (preprint)
arxiv.org
arxiv.org
Principal Curvatures Estimation with Applications to Single Cell Data
Yanlei Zhang
Lydia Mezrag
Xingzhi Sun
Charles Xu
Kincaid MacDonald
Dhananjay Bhaskar
Smita Krishnaswamy
Guy Wolf
Bastian Rieck
2025-02-06
ArXiv (preprint)
doi.org
arxiv.org
Principal Curvatures Estimation with Applications to Single Cell Data
Yanlei Zhang
Lydia Mezrag
Xingzhi Sun
Charles Xu
Kincaid MacDonald
Dhananjay Bhaskar
Smita Krishnaswamy
Guy Wolf
Bastian Rieck
The rapidly growing field of single-cell transcriptomic sequencing (scRNAseq) presents challenges for data analysis due to its massive datas… (see more)ets. A common method in manifold learning consists in hypothesizing that datasets lie on a lower dimensional manifold. This allows to study the geometry of point clouds by extracting meaningful descriptors like curvature. In this work, we will present Adaptive Local PCA (AdaL-PCA), a data-driven method for accurately estimating various notions of intrinsic curvature on data manifolds, in particular principal curvatures for surfaces. The model relies on local PCA to estimate the tangent spaces. The evaluation of AdaL-PCA on sampled surfaces shows state-of-the-art results. Combined with a PHATE embedding, the model applied to single-cell RNA sequencing data allows us to identify key variations in the cellular differentiation.
2025-02-06
ArXiv (preprint)
arxiv.org
arxiv.org
Tackling the Problem of Distributional Shifts: Correcting Misspecified, High-Dimensional Data-Driven Priors for Inverse Problems
Gabriel Missael Barco
Alexandre Adam
Connor Stone
Yashar Hezaveh
Laurence Perreault-Levasseur
Bayesian inference for inverse problems hinges critically on the choice of priors. In the absence of specific prior information, population-… (see more)level distributions can serve as effective priors for parameters of interest. With the advent of machine learning, the use of data-driven population-level distributions (encoded, e.g., in a trained deep neural network) as priors is emerging as an appealing alternative to simple parametric priors in a variety of inverse problems. However, in many astrophysical applications, it is often difficult or even impossible to acquire independent and identically distributed samples from the underlying data-generating process of interest to train these models. In these cases, corrupted data or a surrogate, e.g. a simulator, is often used to produce training samples, meaning that there is a risk of obtaining misspecified priors. This, in turn, can bias the inferred posteriors in ways that are difficult to quantify, which limits the potential applicability of these models in real-world scenarios. In this work, we propose addressing this issue by iteratively updating the population-level distributions by retraining the model with posterior samples from different sets of observations and showcase the potential of this method on the problem of background image reconstruction in strong gravitational lensing when score-based models are used as data-driven priors. We show that starting from a misspecified prior distribution, the updated distribution becomes progressively closer to the underlying population-level distribution, and the resulting posterior samples exhibit reduced bias after several updates.
2025-02-06
The Astrophysical Journal (published)
doi.org
arxiv.org
PANDAS: Improving Many-shot Jailbreaking via Positive Affirmation, Negative Demonstration, and Adaptive Sampling
Avery Ma
Yangchen Pan
Amir-massoud Farahmand
Many-shot jailbreaking circumvents the safety alignment of large language models by exploiting their ability to process long input sequences… (see more). To achieve this, the malicious target prompt is prefixed with hundreds of fabricated conversational turns between the user and the model. These fabricated exchanges are randomly sampled from a pool of malicious questions and responses, making it appear as though the model has already complied with harmful instructions. In this paper, we present PANDAS: a hybrid technique that improves many-shot jailbreaking by modifying these fabricated dialogues with positive affirmations, negative demonstrations, and an optimized adaptive sampling method tailored to the target prompt's topic. Extensive experiments on AdvBench and HarmBench, using state-of-the-art LLMs, demonstrate that PANDAS significantly outperforms baseline methods in long-context scenarios. Through an attention analysis, we provide insights on how long-context vulnerabilities are exploited and show how PANDAS further improves upon many-shot jailbreaking.
2025-02-04
ArXiv (preprint)
doi.org
arxiv.org
PANDAS: Improving Many-shot Jailbreaking via Positive Affirmation, Negative Demonstration, and Adaptive Sampling
Avery Ma
Yangchen Pan
Amir-massoud Farahmand
Many-shot jailbreaking circumvents the safety alignment of large language models by exploiting their ability to process long input sequences… (see more). To achieve this, the malicious target prompt is prefixed with hundreds of fabricated conversational turns between the user and the model. These fabricated exchanges are randomly sampled from a pool of malicious questions and responses, making it appear as though the model has already complied with harmful instructions. In this paper, we present PANDAS: a hybrid technique that improves many-shot jailbreaking by modifying these fabricated dialogues with positive affirmations, negative demonstrations, and an optimized adaptive sampling method tailored to the target prompt's topic. Extensive experiments on AdvBench and HarmBench, using state-of-the-art LLMs, demonstrate that PANDAS significantly outperforms baseline methods in long-context scenarios. Through an attention analysis, we provide insights on how long-context vulnerabilities are exploited and show how PANDAS further improves upon many-shot jailbreaking.
2025-02-04
ArXiv (preprint)
arxiv.org
arxiv.org
Bridging Causality, Individual Fairness, and Adversarial Robustness in the Absence of Structural Causal Model
Ahmad Reza Ehyaei
Golnoosh Farnadi
Samira Samadi
Despite the essential need for comprehensive considerations in responsible AI, factors such as robustness, fairness, and causality are often… (see more) studied in isolation. Adversarial perturbation, used to identify vulnerabilities in models, and individual fairness, aiming for equitable treatment of similar individuals, despite initial differences, both depend on metrics to generate comparable input data instances. Previous attempts to define such joint metrics often lack general assumptions about data and were unable to reflect counterfactual proximity. To address this, our paper introduces a \emph{causal fair metric} formulated based on causal structures encompassing sensitive attributes and protected causal perturbation. To enhance the practicality of our metric, we propose metric learning as a method for metric estimation and deployment in real-world problems in the absence of structural causal models. We also demonstrate the applications of the causal fair metric in classifiers. Empirical evaluation of real-world and synthetic datasets illustrates the effectiveness of our proposed metric in achieving an accurate classifier with fairness, resilience to adversarial perturbations, and a nuanced understanding of causal relationships.
2025-02-03
TMLR (accepted)
openreview.net
openreview.net