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Arxiv
20h
211
Image Credit: Arxiv
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
20h
92
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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
20h
185
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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Arxiv
20h
189
Image Credit: Arxiv
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
20h
327
Image Credit: Arxiv
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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19 Likes
Arxiv
20h
59
Image Credit: Arxiv
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
20h
267
Image Credit: Arxiv
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
20h
29
Image Credit: Arxiv
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
20h
37
Image Credit: Arxiv
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
20h
44
Image Credit: Arxiv
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
20h
204
Image Credit: Arxiv
$\textit{Agents Under Siege}$: 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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Arxiv
20h
148
Image Credit: Arxiv
Rack Position Optimization in Large-Scale Heterogeneous Data Centers
- This work presents a two-tier optimization framework for data center resource management in large-scale heterogeneous environments.
- The framework combines deep reinforcement learning (DRL) with a gradient-based heuristic for optimal rack positioning.
- The high-level DRL agent determines optimal rack type ordering, while the low-level heuristic minimizes movement counts and ensures fault-tolerant resource distribution.
- The proposed approach outperformed the gradient-based heuristic and mixed-integer programming (MIP) solver in terms of objective value and computational efficiency.
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8 Likes
Arxiv
20h
11
Image Credit: Arxiv
Aligning Diffusion Model with Problem Constraints for Trajectory Optimization
- Diffusion models are effective for trajectory optimization but may violate critical constraints without explicit incorporation of constraint information.
- A novel approach aligns diffusion models with problem-specific constraints using a hybrid loss function that measures and penalizes constraint violations during training.
- The re-weighting strategy aligns predicted constraint violations to ground truth statistics, resulting in reduced violations compared to traditional diffusion models.
- This approach can be integrated into the Dynamic Data-driven Application Systems (DDDAS) framework for efficient online trajectory adaptation.
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Arxiv
20h
256
Image Credit: Arxiv
CopyQNN: Quantum Neural Network Extraction Attack under Varying Quantum Noise
- Quantum Neural Networks (QNNs) have shown significant value across domains, with well-trained QNNs representing critical intellectual property often deployed via cloud-based QNN-as-a-Service (QNNaaS) platforms.
- Existing approaches for QNN model extraction attacks have largely overlooked the impact of varying quantum noise inherent in noisy intermediate-scale quantum (NISQ) computers, limiting their effectiveness in real-world settings.
- The CopyQNN framework proposes a three-step data cleaning method to eliminate noisy data based on its noise sensitivity, followed by the integration of contrastive and transfer learning within the quantum domain.
- Experimental results on NISQ computers demonstrate that the practical implementation of CopyQNN outperforms state-of-the-art QNN extraction attacks, achieving an average performance improvement of 8.73% while reducing the number of required queries by 90x.
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15 Likes
Arxiv
20h
349
Image Credit: Arxiv
Spatiotemporal Attention Learning Framework for Event-Driven Object Recognition
- A novel spatiotemporal learning framework for event-based object recognition is presented.
- The framework utilizes a VGG network enhanced with Convolutional Block Attention Module (CBAM).
- The approach achieves comparable performance to state-of-the-art ResNet-based methods while reducing parameter count.
- Experimental results highlight the efficiency and effectiveness of the framework for real-world applications.
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