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Semiengineering
21h
297
Image Credit: Semiengineering
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
22h
332
Image Credit: Medium
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
22h
164
Image Credit: Medium
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
23h
299
Image Credit: Medium
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
1d
348
Image Credit: Arxiv
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
1d
341
Image Credit: Arxiv
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
1d
333
Image Credit: Arxiv
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
1d
330
Image Credit: Arxiv
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
1d
322
Image Credit: Arxiv
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
1d
318
Image Credit: Arxiv
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
1d
41
Image Credit: Arxiv
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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2 Likes
Arxiv
1d
30
Image Credit: Arxiv
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
1d
157
Image Credit: Arxiv
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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9 Likes
Arxiv
1d
67
Image Credit: Arxiv
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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4 Likes
Arxiv
1d
56
Image Credit: Arxiv
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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