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Arxiv

212

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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

227

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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

353

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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

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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

216

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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

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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

189

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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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Arxiv

193

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Data-driven Power Loss Identification through Physics-Based Thermal Model Backpropagation

A novel hybrid framework combining physics-based thermal modeling with data-driven techniques has been developed for accurate power loss identification in power electronics.
The framework leverages a cascaded architecture with a neural network that corrects the outputs of a nominal power loss model using temperature measurements.
Two neural architectures, a bootstrapped feedforward network and a recurrent neural network, were explored, with the feedforward approach achieving superior performance and computational efficiency.
Experimental results demonstrate that the hybrid model reduces temperature estimation errors and power loss prediction errors compared to traditional physics-based approaches, even in the presence of uncertainties.

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Arxiv

334

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Towards Precise Action Spotting: Addressing Temporal Misalignment in Labels with Dynamic Label Assignment

Precise action spotting has attracted attention due to its applications.
Existing methods overlook a challenge of temporal misalignment in ground-truth labels.
A novel dynamic label assignment strategy is proposed to tackle this issue.
The method achieves state-of-the-art performance in conditions with temporal misalignment in labels.

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Arxiv

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Nuclear Microreactor Control with Deep Reinforcement Learning

This study explores the application of deep reinforcement learning for real-time drum control in nuclear microreactors.
Deep reinforcement learning controllers demonstrate similar or better load-following performance compared to traditional PID control.
RL agents can reduce tracking error rate in short transients and maintain accuracy in longer, more complex load-following scenarios.
Multi-agent RL enables independent drum control and maintains reactor symmetry constraints without sacrificing performance.

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Arxiv

273

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Backdoor Detection through Replicated Execution of Outsourced Training

Outsourcing machine learning model training to cloud providers is common practice.
Detecting backdoored models without prior knowledge is challenging.
A client with access to multiple cloud providers can detect deviation by replicating training steps.
The approach is robust and suitable for clients with limited local compute capability.

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Arxiv

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Self-Evolving Visual Concept Library using Vision-Language Critics

Researchers have introduced ESCHER, a visual concept library that aims to improve visual recognition.
ESCHER utilizes a vision-language model as a critic to iteratively refine the concept library.
The approach considers interactions between concepts and their impact on downstream classifiers.
ESCHER does not require human annotations and demonstrates effectiveness in various visual classification tasks.

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Arxiv

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Insight-RAG: Enhancing LLMs with Insight-Driven Augmentation

Retrieval Augmented Generation (RAG) frameworks enhance large language models (LLMs).
Insight-RAG is a framework designed to address limitations of conventional RAG methods.
Insight-RAG employs an LLM to analyze the query and extract informational requirements.
Integrating insight-driven retrieval in RAG enhances performance and expands applicability.

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Arxiv

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Detecting Glioma, Meningioma, and Pituitary Tumors, and Normal Brain Tissues based on Yolov11 and Yolov8 Deep Learning Models

Accurate diagnosis of brain tumors is crucial for treatment planning and medical outcomes.
Manual interpretation of MRI scans is time-consuming and prone to errors.
Researchers propose using YoloV11 and YoloV8 deep learning models to detect glioma, meningioma, and pituitary brain tumors.
By fine-tuning the models, they achieve high accuracies and demonstrate the potential of CNNs in brain tumor detection.

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Arxiv

208

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$\textit{Agents Under Siege}$: Breaking Pragmatic Multi-Agent LLM Systems with Optimized Prompt Attacks

Researchers have developed an adversarial attack that can bypass safety mechanisms in multi-agent Large Language Model (LLM) systems.
The attack optimizes prompt distribution across latency and bandwidth-constrained network topologies to maximize attack success rate while minimizing detection risk.
The method outperforms conventional attacks, exposing critical vulnerabilities in multi-agent systems.
Existing defenses, including variants of Llama-Guard and PromptGuard, fail to prohibit the attack.

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