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Semiengineering

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The Data Dilemma In Semiconductor Testing And Why It Matters: Part 1

  • Machine learning is essential in the semiconductor industry to optimize test flows, improve quality, and lower costs.
  • Data Feed Forward (DFF) involves sharing device test data across different facilities, but implementing it is a challenge.
  • Transferring data in semiconductor manufacturing requires secure platforms and extensive data preparation efforts.
  • DFF is crucial for real-time analytics in semiconductor testing, enabling adjustments, improving efficiency, and maximizing yield.

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Medium

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AlphaFold 3, Demystified: A Comprehensive Technical Breakdown of Its Architecture and Design

  • AlphaFold 3's architecture consists of four main components: Multiple Sequence Alignments (MSA), Template Module, MSA Module, and Pairformer Module.
  • The model learns through three types of losses: Distance Accuracy, Atomic Relationships, and Confidence Prediction, using sophisticated attention mechanisms with 'pair bias' for consistent geometric predictions.
  • The diffusion module refines atomic coordinates iteratively starting from random coordinates, conditioned on molecular sequence and evolutionary information, with computational efficiency achieved through sparse attention patterns.
  • AlphaFold 3's integration of evolutionary information, structural knowledge, and deep learning techniques like diffusion models results in unprecedented accuracy in predicting molecular structures, setting a new standard in computational structural biology.

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Medium

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ML.NET vs Python: Which One Should You Use for Machine Learning in 2025?

  • ML.NET is a different approach from Python for machine learning, designed to integrate seamlessly with .NET applications.
  • ML.NET allows developers to stay within the .NET ecosystem, avoiding the need to switch languages or tools.
  • Python, on the other hand, is known for its conciseness, while ML.NET offers direct integration into .NET applications for easy production deployment.
  • Tutorials for both ML.NET and Python are available on Archety.dev to help users learn and choose based on their preferences.

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Medium

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97% of Python Devs Still Use pip + venv — uv Makes That Obsolete

  • A new Python tool called uv, written in Rust, is gaining popularity among Python developers.
  • uv replaces multiple existing tools like pip, venv, pip-tools, and pipx, offering a faster alternative.
  • Python developers are finding uv useful in both small and large projects, enhancing their workflow.
  • Installing uv globally on your machine enables you to streamline package and environment management in Python.

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Arxiv

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CellCLIP -- Learning Perturbation Effects in Cell Painting via Text-Guided Contrastive Learning

  • High-content screening (HCS) assays based on Cell Painting enable the study of cells' responses to perturbations on a large scale.
  • Recent advancements in cross-modal contrastive learning can be used to align perturbations with their morphological effects in a unified latent space.
  • CellCLIP, a cross-modal contrastive learning framework for HCS data, uses pre-trained image encoders and a unique channel encoding scheme to capture relationships between microscopy channels and natural language encoders for perturbations.
  • CellCLIP surpasses current open-source models, excelling in cross-modal retrieval, biological downstream tasks, and achieving notable reductions in computation time.

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Arxiv

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Mutual-Taught for Co-adapting Policy and Reward Models

  • A new method called Mutual-Taught has been proposed to address challenges in preference optimization of large language models (LLMs).
  • Mutual-Taught is a self-training method that iteratively improves both the policy model (PM) and the reward model (RM) without additional human annotation, similar to the expectation-maximization (EM) algorithm.
  • The PM is updated using feedback from the RM in the E-step to approximate the optimal preference distribution, while the RM is updated in the M-step using training data constructed from PM outputs.
  • Experimental results show that Mutual-Taught leads to consistent improvements in both the PM and RM, with the 8B policy model achieving a 54.1% win rate on AlpacaEval-2 and the 8B reward model performing well on RewardBench.

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Arxiv

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GLProtein: Global-and-Local Structure Aware Protein Representation Learning

  • Proteins are crucial in biological systems and understanding their functions is essential.
  • GLProtein is a novel framework for protein pre-training that integrates global structural similarity and local amino acid details for improved prediction accuracy and functional insights.
  • GLProtein combines protein-masked modeling, triplet structure similarity scoring, protein 3D distance encoding, and substructure-based amino acid molecule encoding.
  • Experimental results show that GLProtein surpasses previous methods in various bioinformatics tasks like predicting protein-protein interaction and contact prediction.

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Arxiv

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dLLM-Cache: Accelerating Diffusion Large Language Models with Adaptive Caching

  • A new paradigm of diffusion-based Large Language Models (dLLMs) has emerged in text generation, offering advantages over Autoregressive Models (ARMs).
  • Traditional ARM acceleration techniques like Key-Value caching are not suitable for dLLMs due to their bidirectional attention mechanism causing high inference latency.
  • To address this, dLLM-Cache, a training-free adaptive caching framework, has been introduced, combining prompt caching with response updates for efficient computation reuse.
  • Experiments on dLLMs like LLaDA 8B and Dream 7B have shown that dLLM-Cache speeds up inference by up to 9.1 times without sacrificing output quality, bringing dLLM latency closer to ARMs.

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Arxiv

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Optimal patient allocation for echocardiographic assessments

  • Scheduling echocardiographic exams in a hospital is challenging due to non-deterministic factors and asymmetric resource constraints between fetal and non-fetal patient streams.
  • Researchers conducted preprocessing on operational data from Stanford University's Lucile Packard Children's Hospital to estimate patient no-show probabilities and derive empirical distributions of arrival times and exam durations.
  • A discrete-event stochastic simulation model was developed using SimPy and integrated with the Gymnasium Python library to evaluate different resource allocation strategies.
  • Results showed that on-the-fly allocation generally performed better in adapting to patient variability and resource constraints, leading to the development of a reinforcement learning-based optimal dynamic allocation policy for improving echo lab efficiency.

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Arxiv

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Pairwise Calibrated Rewards for Pluralistic Alignment

  • Proposing a method for capturing diverse human preferences by utilizing a distribution over multiple reward functions.
  • Introducing a strategy to learn this distribution directly from pairwise preferences without predefined groups or annotator identifiers.
  • Focusing on pairwise calibration where the proportion of reward functions favoring a response aligns with the preferences of annotators.
  • Validating the effectiveness of the proposed method in representing pluralistic values through improved calibration results.

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Arxiv

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LT-PINN: Lagrangian Topology-conscious Physics-informed Neural Network for Boundary-focused Engineering Optimization

  • Physics-informed neural networks (PINNs) are powerful tools for topology optimization and determining physical solutions.
  • A new approach called Lagrangian topology-conscious PINNs (LT-PINNs) eliminates the need for manual interpolation in determining optimal topologies and physical solutions.
  • LT-PINNs introduce specialized loss functions ensuring sharp and accurate boundary representations for complex geometries.
  • LT-PINNs demonstrate superior performance in reducing errors, handling arbitrary boundary conditions, and inferring clear topology boundaries without manual interpolation.

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Arxiv

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Reward Is Enough: LLMs Are In-Context Reinforcement Learners

  • Reinforcement learning (RL) emerges in LLM's (Large Language Model) inference time, known as in-context RL (ICRL).
  • A novel multi-round prompting framework called ICRL prompting is proposed to prompt LLMs for task completion.
  • LLM's response quality increases as the context grows, maximizing the scalar reward signal in inference time like an RL algorithm.
  • ICRL prompting shows significant performance improvements in benchmarks such as Game of 24, creative writing, and ScienceWorld, even when LLM generates its own reward signals.

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Arxiv

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Wine Quality Prediction with Ensemble Trees: A Unified, Leak-Free Comparative Study

  • Accurate and reproducible wine-quality assessment is essential for production control.
  • A unified benchmark of five ensemble learners (Random Forest, Gradient Boosting, XGBoost, LightGBM, CatBoost) was conducted on Vinho Verde red- and white-wine datasets.
  • Gradient Boosting showed the highest accuracy in the study, followed closely by Random Forest and XGBoost.
  • The study recommended Random Forest as the most cost-effective model for wine-quality prediction.

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Arxiv

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ExplainBench: A Benchmark Framework for Local Model Explanations in Fairness-Critical Applications

  • ExplainBench is an open-source benchmarking suite designed for evaluating local model explanations in critical domains like criminal justice, finance, and healthcare.
  • It aims to standardize and facilitate the comparative assessment of explanation techniques like SHAP, LIME, and counterfactual methods, especially in fairness-sensitive contexts.
  • ExplainBench offers unified wrappers for explanation algorithms, integrates pipelines for model training and explanation generation, and supports evaluation using metrics like fidelity, sparsity, and robustness.
  • This framework includes a graphical interface for interactive exploration, is packaged as a Python module, and is demonstrated on datasets like COMPAS, UCI Adult Income, and LendingClub to showcase its utility in promoting interpretable machine learning and accountability in AI systems.

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Arxiv

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Extending AALpy with Passive Learning: A Generalized State-Merging Approach

  • AALpy, an open-source automata learning library in Python, has added a generalized state-merging approach for passive learning.
  • The library offers advanced algorithms for different automaton types, including deterministic and probabilistic automata.
  • The new addition allows for a flexible implementation of the red-blue framework by using a common internal representation for various automaton types.
  • Implementing state-merging algorithms with AALpy is simplified, requiring minimal effort for defining compatibility criteria and scoring.

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