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PPO agent for network control

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I built a PPO-Agent to control flows inside a physical network. The agent controls the 15 control variables, which in physical world would mean how strong we are pumping the medium inside the network. It is working after 25 million environment steps. I was testing different reward functions and so far the best was something like following: reward = -1 * tanh(physical_violations_in_network) + 0.05 * tanh(violation_improvement_from_previous_step) – 0.07 * tanh(violation_deterioration_from_previous_step) I made the improvement coef and deterioration coef […]

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Photon Releases Spectrum: An Open-Source TypeScript Framework that Deploys AI Agents Directly to iMessage, WhatsApp, and Telegram

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For all the progress made in AI agent development over the past few years, one fundamental problem has remained largely unsolved: most people never actually interact with agents. They live behind developer dashboards, inside specialized apps that users are asked to download, and within chat interfaces that the majority of the world’s population will never visit. The models are good. The reasoning capabilities are extraordinary. But the distribution is broken. Photon, an infrastructure company focused on reliable, low-latency […]

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The role of AI in modern forex bot development

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Artificial intelligence has become a defining force in financial markets. And currency trading is no exception. The rise of the AI-powered forex bot reflects a change toward automated systems capable of processing vast amounts of market data and identifying patterns beyond the reach of manual analysis. As global foreign exchange markets operate around the clock and generate enormous streams of information, traders increasingly rely on intelligent tools that can analyse, interpret and act on market signals in real […]

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SAB-DeepLabV3+: A Semantic Segmentation Framework for Mapping Maize Waterlogging from Single-Date Multispectral Imagery

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Rapid identification of maize waterlogging is essential for post-disaster agricultural assessment, but most existing methods rely on multi-temporal imagery that is often unavailable immediately after extreme rainfall events. This study proposes SAB-DeepLabV3+, a semantic segmentation model for mapping waterlogged maize from single-date multispectral imagery within pre-extracted maize planting areas. Built on DeepLabV3+, the model integrates three task-specific modules: a Spectral-Spatial Information Enhancement Module to improve feature discrimination under spectral mixing, an Adaptive Multi-Scale Pooling Module to capture heterogeneous […]

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RADS: Reinforcement Learning-Based Sample Selection Improves Transfer Learning in Low-resource and Imbalanced Clinical Settings

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A common strategy in transfer learning is few shot fine-tuning, but its success is highly dependent on the quality of samples selected as training examples. Active learning methods such as uncertainty sampling and diversity sampling can select useful samples. However, under extremely low-resource and class-imbalanced conditions, they often favor outliers rather than truly informative samples, resulting in degraded performance. In this paper, we introduce RADS (Reinforcement Adaptive Domain Sampling), a robust sample selection strategy using reinforcement learning (RL) […]

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INT3 compression+fused metal kernels [R]

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Hey guys, I am a researcher and solo founder. I compress models with INT3 at +0.14 nats and built a 2-bit KV cache for long-horizon tasks. I shipped both (INT3 model + INT2 KV) with custom fused Metal kernels for Mac (M-series). Currently Qwen 7B is available in preview. #install brew install reinforceai/spiral/spiral #chat spiral-chat I am optimizing kernels further and working on Triton kernels for GPU support. There is still more room to pack more efficiently, I […]

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