digitado

This study proposes a hybrid artificial intelligence framework for graduate subject allocation that enhances fairness, transparency, and operational efficiency in higher education. By integrating rule-based reasoning with advanced deep learning models, the framework supports data-driven decision-making for academic suitability, workload equity, and research alignment, while embedding Explainable Artificial Intelligence (XAI) to facilitate digital transformation in Thai universities. Traditional subject allocation processes in graduate programs are often manual, time-consuming, and subject to subjective judgment, thereby limiting their capacity to […]

Ver mais

Overparameterized machine learning (ML) methods such as neural networks may be prohibitively resource intensive for devices with limited computational capabilities. Hyperdimensional computing (HDC) is an emerging resource efficient and low-complexity ML method that allows hardware efficient implementations of (re-)training and inference procedures. In this paper, we propose a maximum-margin HDC classifier, which significantly outperforms baseline HDC methods on several benchmark datasets. Our method leverages a formal relation between HDC and support vector machines (SVMs) that we established for […]

Ver mais

Interactive recommender systems (IRS) are increasingly optimized with Reinforcement Learning (RL) to capture the sequential nature of user-system dynamics. However, existing fairness-aware methods often suffer from a fundamental oversight: they assume the observed user state is a faithful representation of true preferences. In reality, implicit feedback is contaminated by popularity-driven noise and exposure bias, creating a distorted state that misleads the RL agent. We argue that the persistent conflict between accuracy and fairness is not merely a reward-shaping […]

Ver mais

Continual learning is a long-standing challenge in robot policy learning, where a policy must acquire new skills over time without catastrophically forgetting previously learned ones. While prior work has extensively studied continual learning in relatively small behavior cloning (BC) policy models trained from scratch, its behavior in modern large-scale pretrained Vision-Language-Action (VLA) models remains underexplored. In this work, we found that pretrained VLAs are remarkably resistant to forgetting compared with smaller policy models trained from scratch. Simple Experience […]

Ver mais

New York, United States, March 3rd, 2026/Chainwire/–Threshold Network, the decentralized blockchain protocol behind tBTC, has introduced an update to its decentralized application featuring an all-in-one Unified Bitcoin App that enables users to route Bitcoin across major chains through a single interface. This new unified routing interface brings minting, redeeming, bridging, tracking, and native BTC swaps into a single application: The Threshold App. Users can now move Bitcoin across ecosystems through a coordinated system, rather than stitching together multiple […]

Ver mais

What are the numbers made of? More precisely, what are prime numbers made of? I posed this question to myself on the evening of August 19, 2025, which prompted prolonged introspection and profound contemplation. Then, I began constructing a numerical pyramid with prime numbers. The number one took the place of the central axis. Therefore, it is possible that large prime numbers could be surrounded by prime numbers on either side of one. However, this property extends to […]

Ver mais

I’ve been building a system to create physics-based humanoid characters in Unity that can learn through reinforcement learning — and you can physically interact with them in mixed reality on Quest. Today I’m open-sourcing the three packages that make it up. What it does: synth-core — Take any Daz Genesis 8 or Mixamo character, run it through an editor wizard (or one-click right-click menu), and get a fully physics-simulated humanoid with MuJoCo rigid-body dynamics, mesh-based collision geometry, configurable […]

Ver mais

Relational Databases (RDBs) are the backbone of modern business, yet they lack foundation models comparable to those in text or vision. A key obstacle is that high-quality RDBs are private, scarce and structurally heterogeneous, making internet-scale pre-training infeasible. To overcome this data scarcity, We introduce $textbf{RDB-PFN}$, the first relational foundation model trained purely via $textbf{synthetic data}$. Inspired by Prior-Data Fitted Networks (PFNs) where synthetic data generated from Structural Causal Models (SCMs) enables reasoning on single tables, we design […]

Ver mais