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Arxiv
1d
23
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Benchmarking Federated Machine Unlearning methods for Tabular Data
- This paper focuses on benchmarking machine unlearning methods for tabular data within a federated learning (FL) setting.
- The study explores unlearning at the feature and instance levels using machine learning models.
- The benchmarking methodology evaluates various unlearning algorithms and compares their fidelity, certifiability, and computational efficiency.
- The results show that tree-based models excel in certifiability, while gradient-based methods offer improved computational efficiency.
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Arxiv
1d
139
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CFIRE: A General Method for Combining Local Explanations
- Researchers propose a new eXplainable AI algorithm for computing global decision rules.
- The algorithm combines XAI methods with closed frequent itemset mining.
- By addressing the disagreement problem, the algorithm accommodates different local explainers.
- Evaluation of the algorithm demonstrates its robustness and improved performance in generating compact and complete rules.
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Arxiv
1d
0
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Personalized Federated Training of Diffusion Models with Privacy Guarantees
- The scarcity of accessible, compliant, and ethically sourced data presents a challenge for adopting AI in sensitive fields like healthcare and finance.
- Diffusion models offer a solution for generating diverse synthetic data, which can be used as an alternative to restricted public datasets.
- A novel federated learning framework is introduced for training diffusion models on decentralized private datasets.
- The framework ensures robust differential privacy guarantees and produces high-quality samples, reducing biases and imbalances in synthetic data.
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Arxiv
1d
302
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MedPix 2.0: A Comprehensive Multimodal Biomedical Data set for Advanced AI Applications with Retrieval Augmented Generation and Knowledge Graphs
- The paper introduces the MedPix 2.0 data set, a comprehensive multimodal biomedical data set for advanced AI applications.
- The lack of high-quality medical data sets and the need for multimodal data sets due to the rise of large multimodal models are addressed.
- The paper details the process of building the MedPix 2.0 data set, which involves extracting visual and textual data from the original MedPix data set.
- In addition to the data set, a GUI and a knowledge graph extension are developed for efficient navigation and medical decision support.
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Arxiv
1d
11
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Deep Learning-Based Hypoglycemia Classification Across Multiple Prediction Horizons
- Type 1 diabetes (T1D) management can be enhanced through predictive machine learning algorithms.
- This study integrates short- and long-term prediction horizons within a single classification model to enhance decision support.
- LSTM models outperformed ResNet models in classifying nine classes, but longer prediction horizons remain challenging.
- A population-based six-class model detected at least 60% of hypoglycemia events.
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Arxiv
1d
104
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I'm Sorry Dave: How the old world of personnel security can inform the new world of AI insider risk
- Organisations are rapidly adopting artificial intelligence (AI) tools to perform tasks previously undertaken by people.
- There is no meaningful interplay between the rapidly evolving domain of AI and the traditional world of personnel security, posing a problem.
- Some concepts and approaches used to address security risks from human insiders are applicable to the emerging risks from AI insiders.
- AI can be used defensively to protect against both human and AI insiders.
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Arxiv
1d
124
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Enhance Vision-based Tactile Sensors via Dynamic Illumination and Image Fusion
- Vision-based tactile sensors use structured light to measure deformation in their elastomeric interface.
- This work explores the use of dynamic illumination patterns and image fusion techniques to enhance the quality of vision-based tactile sensors.
- By capturing multiple measurements with different illumination patterns and fusing them together, a higher-quality measurement can be obtained.
- Experimental results demonstrate significant improvements in image contrast, sharpness, and background difference, which can be retroactively applied to existing sensors or integrated into new hardware designs.
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Arxiv
1d
128
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SandboxEval: Towards Securing Test Environment for Untrusted Code
- Large language models (LLMs) have the potential to produce malicious code, posing risks to assessment infrastructure.
- SandboxEval is a test suite designed to evaluate the security and confidentiality of test environments for LLM-generated code.
- The suite focuses on vulnerabilities related to sensitive information exposure, filesystem manipulation, external communication, and other dangerous operations.
- By deploying SandboxEval, developers can gain valuable insights to improve assessment infrastructure and identify risks associated with LLM execution.
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Arxiv
1d
217
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Celler:A Genomic Language Model for Long-Tailed Single-Cell Annotation
- Recent breakthroughs in single-cell technology have led to the need for efficient annotation of long-tailed single-cell data pertaining to disease conditions.
- To address this challenge, Celler, a generative pre-training model, has been introduced that incorporates the Gaussian Inflation (GInf) Loss function and Hard Data Mining (HDM) strategy.
- The GInf Loss function dynamically adjusts sample weights, improving the model's ability to learn from rare categories and reducing the risk of overfitting for common categories.
- The HDM strategy targets difficult-to-learn minority data samples, significantly improving the model's predictive accuracy.
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Arxiv
1d
232
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A multi-locus predictiveness curve and its summary assessment for genetic risk prediction
- With the advancement of high-throughput genotyping and sequencing technologies, a need arises to evaluate the role of genetic predictors in disease prediction.
- A multi-marker predictiveness curve is proposed to measure the combined effects of multiple genetic variants in risk prediction models for complex diseases.
- The predictiveness curve is connected with the ROC curve and Lorenz curve.
- The predictiveness U is introduced as a summary index to evaluate the predictive ability of risk prediction models, and it outperformed other summary indices in terms of unbiasedness and robustness.
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Arxiv
1d
360
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Improving Diseases Predictions Utilizing External Bio-Banks
- Machine learning can enhance disease predictions and uncover biologically meaningful associations, even with limited data.
- LightGBM models trained on a dataset of 10K are used to impute metabolomics features.
- Survival analysis is applied to assess the impact of imputed metabolomics features on disease-related risk factors.
- Integration of survival analysis and genetic studies with machine learning can uncover valuable biomedical insights.
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Arxiv
1d
73
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Imbalanced malware classification: an approach based on dynamic classifier selection
- This study addresses the issue of class imbalance in malware detection on mobile devices.
- The study evaluates various machine learning strategies for detecting malware in Android applications.
- The proposed approach focuses on dynamic classifier selection algorithms, which have shown superior performance.
- The empirical analysis demonstrates the effectiveness of the KNOP algorithm using a pool of Random Forest.
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Arxiv
1d
221
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GAL-MAD: Towards Explainable Anomaly Detection in Microservice Applications Using Graph Attention Networks
- The transition to microservices has revolutionized software architectures, offering enhanced scalability and modularity.
- Anomaly detection is crucial for maintaining performance and functionality in microservice applications.
- A novel anomaly detection model called GAL-MAD is proposed, leveraging Graph Attention and LSTM architectures.
- GAL-MAD outperforms state-of-the-art models on the RS-Anomic dataset, achieving higher accuracy and recall.
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Arxiv
1d
97
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Improving Predictions of Convective Storm Wind Gusts through Statistical Post-Processing of Neural Weather Models
- Issuing timely severe weather warnings helps mitigate potentially disastrous consequences.
- Neural Weather Models (NWMs) offer a computationally inexpensive and fast approach for forecasting atmospheric environments.
- The study applied statistical and deep learning post-processing methods to forecast wind gusts using NWMs.
- Results confirmed the added value of NWMs for extreme wind forecasting and designing more responsive early-warning systems.
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Arxiv
1d
194
Image Credit: Arxiv
Contextualize-then-Aggregate: Circuits for In-Context Learning in Gemma-2 2B
- In-Context Learning (ICL) is an intriguing ability of large language models (LLMs).
- Research finds that Gemma-2 2B uses a two-step strategy, contextualize-then-aggregate, for task information assembly.
- In the lower layers, the model builds up representations of individual fewshot examples, contextualized by preceding examples.
- In the higher layers, these representations are aggregated to identify the task and prepare predictions.
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