An information maximization view on the $beta$-VAE objective

An information maximization view on the $beta$-VAE objective

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guest post with Dóra Jámbor This is a half-guest-post written jointly with Dóra, a fellow participant in a reading group where we recently discussed the original paper on $beta$-VAEs: Irina Higgins et al (ICLR 2017): $beta$-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework On the surface of it, $beta$-VAEs are a straightforward extension of VAEs where we are allowed to directly control the tradeoff between the reconstruction and KL loss terms. In an attempt to better […]

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TextBridgeGNN: Pre-training Graph Neural Network for Cross-Domain Recommendation via Text-Guided Transfer

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arXiv:2601.02366v1 Announce Type: new Abstract: Graph-based recommendation has achieved great success in recent years. The classical graph recommendation model utilizes ID embedding to store essential collaborative information. However, this ID-based paradigm faces challenges in transferring to a new domain, making it hard to build a pre-trained graph recommendation model. This phenomenon primarily stems from two inherent challenges: (1) the non-transferability of ID embeddings due to isolated domain-specific ID spaces, and (2) structural incompatibility between heterogeneous interaction graphs across […]

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HBase in Big Data: The Heart of Real-Time Data Management

HBase in Big Data: The Heart of Real-Time Data Management

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The world of Big Data is vast, diverse, and fast-moving, no doubt about it. And as organisations in India and across the globe deal with massive data every second, the need for systems that can store, manage, and process data efficiently has grown rapidly. One such system is HBase in Big Data, an open-source, distributed, non-relational database designed to handle huge amounts of structured and semi-structured data. Have you ever wondered how Facebook, LinkedIn, or Flipkart stores millions of records and […]

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AI-Powered Predictive Maintenance via Sensor Fusion and Machine Learning in Downtime Reduction and Equipment Efficiency in Industry 4.0 Manufacturing Plants

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The integration of artificial intelligence-powered predictive maintenance solutions in manufacturing plants is revolutionizing asset management by substantially reducing downtime and enhancing equipment efficiency. By utilizing sensor fusion combining data from multiple sources such as vibration, temperature, and pressure sensors with advanced machine learning algorithms, manufacturers are able to continuously monitor machine health and forecast potential failures long before they occur. This data-driven strategy shifts maintenance from a reactive or scheduled paradigm to a proactive and dynamic process, resulting […]

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Approximate parameter inference in a stochastic reaction-diffusion model

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We present an approximate inference approach to parameter estimation in a spatio-temporal stochastic process of the reaction-diffusion type. The continuous space limit of an inference method for Markov jump processes leads to an approximation which is related to a spatial Gaussian process. An efficient solution in feature space using a Fourier basis is applied to inference on simulational data.

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Differentiation Between Faults and Cyberattacks through Combined Analysis of Cyberspace Logs and Physical Measurements

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arXiv:2601.03289v1 Announce Type: new Abstract: In recent years, cyberattacks – along with physical faults – have become an increasing factor causing system failures, especially in DER (Distributed Energy Resources) systems. In addition, according to the literature, a number of faults have been reported to remain undetected. Consequently, unlike anomaly detection works that only identify abnormalities, differentiating undetected faults and cyberattacks is a challenging task. Although several works have studied this problem, they crucially fall short of achieving an […]

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The Rational Bull Elk

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I watch a lot of nature documentaries. I’m not very choosy about the animals covered, whether whales, moles, lions, ants, chameleons, blowfish, or mosquitoes. I’m even fascinated by footage of bacteria under a microscope. I’m usually immersed as I sit in front of my large-screen television, so long as I learn something about the intricacies of the species filmed in vibrant colors. What do they eat and how do they avoid being eaten? What are their life expectancies, […]

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Performance of a Deep Learning-Based Segmentation Model for Pancreatic Tumors on Public Endoscopic Ultrasound Datasets

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Background: Pancreatic cancer is one of the most aggressive cancers, with poor survival rates. Endoscopic ultrasound (EUS) is a key diagnostic modality, but its effectiveness is constrained by operator subjectivity. This study evaluates a Vision Transformer-based deep learning segmentation model for pancreatic tumors. Methods: A segmentation model using the USFM framework with a Vision Transformer backbone was trained and validated with 17,367 EUS images (from two public datasets) in 5-fold cross-validation. The model was tested on an independent […]

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UAE Issues AI-Themed Stamps as Artificial Intelligence Enters School Curriculum

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Key Highlights UAE Marks Curriculum Rollout With Commemorative AI-Themed Stamps The United Arab Emirates is signalling its long-term ambitions in artificial intelligence not through a white paper or policy speech, but through postage stamps. The national postal service, Emirates Post, has issued a four-stamp commemorative collection marking the integration of artificial intelligence as a formal subject within the UAE’s school curriculum. Developed in collaboration with the Ministry of Education, the release coincides with the nationwide rollout of AI […]

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