digitado

A Unified Namespace (UNS) gives every sensor, actuator, and PLC a path in a shared ISA-95 hierarchy. Data from every facility flows into one namespace, whether the transport is MQTT, OPC UA, or Kafka. The structure problem? Solved. But structure alone doesn’t make those paths queryable. Once you store millions of timestamped values in a dedicated time-series database, the tag_id in each row is just a reference, and the hierarchy that gives it meaning lives somewhere else. Every contextual question (“which […]

Ver mais

The integration of artificial intelligence into financial services represents an operational shift for banking institutions operating across global markets. Industry implementation accelerates as financial firms utilize Generative AI to adjust user experiences across digital channels. This transition from touch-based interfaces to conversational frameworks requires oversight of system performance, rigorous security protocols, and secure data handling capabilities. Amar Kant Jha, a Lead Software Engineer with over fourteen years of experience in mobile application architecture, focuses extensively on developing iOS […]

Ver mais

Graph neural networks face two fundamental challenges rooted in the linear structure of Euclidean vector spaces: (1) Current architectures represent geometry through vectors (directions, gradients), yet many tasks require matrix-valued representations that capture relationships between directions-such as how atomic orientations covary in a molecule. These second-order representations are naturally captured by points on the symmetric positive definite matrices (SPD) manifold; (2) Standard message passing applies shared transformations across edges. Sheaf neural networks address this via edge-specific transformations, but […]

Ver mais

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 […]

Ver mais

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 […]

Ver mais

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 […]

Ver mais

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 […]

Ver mais

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) […]

Ver mais