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Digital Solutions for Managing Supply Chains in Manufacturing

  • Supply chain management (SCM) ensures seamless and cost-effective production processes.
  • Digital tools streamline operations, reduce costs, and enhance collaboration.
  • Automation, real-time tracking, and AI-powered insights optimize supply chain management.
  • Digitizing supply chains is essential for competitiveness and resilience in manufacturing.

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Microsoft Researchers Release AIOpsLab: An Open-Source Comprehensive AI Framework for AIOps Agents

  • Microsoft researchers, along with researchers from other institutions, have developed AIOpsLab, an open-source framework for evaluating AIOps agents.
  • AIOpsLab aims to provide standardized and reproducible benchmarks for testing and improving AIOps agents.
  • It integrates real-world workloads, fault injection capabilities, and interfaces to simulate production-like scenarios.
  • The framework offers valuable insights into agent performance and aids in continuous improvement of fault localization and resolution capabilities.

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Meet LLMSA: A Compositional Neuro-Symbolic Approach for Compilation-Free, Customizable Static Analysis with Reduced Hallucinations

  • Researchers have developed LLMSA, a neuro-symbolic framework for customizable static analysis.
  • LLMSA enables compilation-free functionality and full customization, addressing limitations of traditional static analysis tools.
  • The framework combines symbolic and neural elements to perform static analysis tasks efficiently.
  • LLMSA shows promising results in various static analysis tasks, outperforming dedicated tools and improving computational efficiency.

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What Is an AI Generalist? Growth and Scope in a Transforming World

  • An AI generalist is someone or something that possesses a diverse skill set across areas like machine learning, natural language processing, and computer vision.
  • AI generalists can handle a wide variety of tasks and are capable of adapting, pivoting, and solving problems from multiple angles.
  • The demand for AI generalists is growing rapidly as they are able to bridge diverse fields and transform roles in various industries.
  • AI generalists contribute to innovation by combining knowledge, creativity, and adaptability to tackle complex challenges and reshape industries.

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OpenAI’s o3 vs o1: The Dawn of Hyper-Intelligent AI

  • OpenAI's o3 is a hyper-intelligent AI with simulated reasoning capability, surpassing its predecessor o1.
  • o3 outperforms benchmarks and is available in two versions, targeting large tech companies and research institutions.
  • The pricing of o3 places it out of reach for individual use, but it represents the future of AI's ability to handle complex problems.
  • o3 showcases new standards in AI safety and paves the way for advanced AI reasoning becoming more accessible in the future.

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The Dawn of AGI? OpenAI’s New o3 Model Challenges Human Intelligence

  • OpenAI's new o3 model has surpassed human performance on the ARC Benchmark, a test for core reasoning skills.
  • o3 scored 76% on low-compute settings, and 88% on high-compute settings, outperforming humans on reasoning-based tasks.
  • The cost of using the o3 model in high-tuning mode reaches up to $1,000 per task, but advancements suggest AI democratization in the future.
  • While some see it as a milestone towards AGI, critics argue that true AGI requires models to effortlessly solve novel challenges.

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Arxiv

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A Universal Model for Human Mobility Prediction

  • Researchers have developed a universal model for human mobility prediction to overcome the limitations of task-specific models.
  • The model, called UniMob, can be applied to both individual trajectory and crowd flow data.
  • UniMob utilizes a multi-view mobility tokenizer and a diffusion transformer architecture for unified sequential modeling.
  • Experiments on real-world datasets show that UniMob outperforms state-of-the-art baselines, especially in noisy and scarce data scenarios.

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Arxiv

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Parametric $\rho$-Norm Scaling Calibration

  • Output uncertainty indicates whether the probabilistic properties reflect objective characteristics of the model output.
  • A post-processing parametric calibration method called $ ho$-Norm Scaling is introduced to mitigate overconfidence in limited data sets.
  • The method expands the calibrator expression to preserve accuracy while reducing excessive amplitude.
  • Probability distribution regularization is included to ensure the instance-level uncertainty distribution resembles the distribution before calibration.

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Arxiv

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Tokenphormer: Structure-aware Multi-token Graph Transformer for Node Classification

  • Graph Neural Networks (GNNs) are widely used in graph data mining tasks.
  • Traditional GNNs face limitations in terms of over-smoothing and over-squashing, which limit the receptive field in message passing processes.
  • To address these limitations, a new approach called Structure-aware Multi-token Graph Transformer (Tokenphormer) is proposed.
  • Tokenphormer generates multiple tokens to capture local and structural information, exploring global information at different levels of granularity.

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Arxiv

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TinyLLM: A Framework for Training and Deploying Language Models at the Edge Computers

  • Language models have gained significant interest due to their general-purpose capabilities.
  • However, large language models impose stringent requirements on computing systems.
  • To address these challenges, researchers explored the possibility of using smaller models (~30-120M parameters) for specific tasks.
  • They developed a framework that allows users to train and deploy these small models on edge devices.

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Arxiv

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Re-evaluating Group Robustness via Adaptive Class-Specific Scaling

  • The authors propose a class-specific scaling strategy for group distributionally robust optimization to improve robust accuracies and mitigate spurious correlations and dataset bias.
  • They further develop an instance-wise adaptive scaling technique to alleviate the trade-off between robust and average accuracies.
  • A na"ive ERM baseline using the proposed class-specific scaling technique matches or even outperforms recent debiasing methods.
  • They also introduce a novel unified metric to quantify the trade-off between robust and average accuracies.

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Arxiv

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Efficient Fine-Tuning and Concept Suppression for Pruned Diffusion Models

  • Efficient Fine-Tuning and Concept Suppression for Pruned Diffusion Models
  • Diffusion generative models have advanced significantly, but they have become larger and more complex, creating computational challenges in resource-constrained scenarios.
  • Pruning and knowledge distillation can reduce computational demands while preserving generation quality, but they can also propagate undesirable behaviors.
  • A new bilevel optimization framework is proposed to consolidate fine-tuning and unlearning processes, selectively suppressing the generation of unwanted content in diffusion models.

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Arxiv

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Large Language Models on Small Resource-Constrained Systems: Performance Characterization, Analysis and Trade-offs

  • Generative AI, such as Large Language Models (LLMs), is becoming more accessible for the general consumer.
  • However, there are concerns regarding reliance on network access, privacy, and security risks.
  • To address this, researchers are optimizing LLMs for running locally on edge devices.
  • A recent study focuses on characterizing commercially available embedded hardware (Jetson Orin) for LLMs and provides a utility for batch testing.

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Arxiv

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GeoPro-Net: Learning Interpretable Spatiotemporal Prediction Models through Statistically-Guided Geo-Prototyping

  • GeoPro-Net is an intrinsically interpretable spatiotemporal model for forecasting spatiotemporal events.
  • It introduces a novel Geo-concept convolution operation and employs statistical tests to extract predictive patterns.
  • GeoPro-Net achieves better interpretability while maintaining competitive prediction performance compared to baselines.
  • Experiments and case studies on real-world datasets demonstrate the effectiveness of GeoPro-Net.

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Arxiv

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Spatiotemporally Coherent Probabilistic Generation of Weather from Climate

  • Local climate information is crucial for impact assessment and decision-making.
  • Current statistical downscaling methods infer small-scale phenomena as temporally decoupled spatial patches.
  • A novel generative approach based on a score-based diffusion model is presented.
  • The model generates spatiotemporally coherent weather dynamics aligned with global climate output.

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