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Stereo matching has witnessed rapid advances on curated benchmarks, yet deploying models in unconstrained real-world environments remains a fundamental challenge. This paper presents a sparse self-prompt guided network (SSPGNet) for stereo matching with strong generalization across diverse environments. Our core innovation lies in a sparse self-prompt guidance mechanism: 1) a sparse disparity map, used as a prompt, is self-estimated from visual foundation model features via cost aggregation; and 2) the sparse disparity is progressively refined into dense disparity […]

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Being over 20 years old in the sphere of enterprise technology is a reality that can no longer be ignored: the very thing that once made enterprises stable—legacy systems—has now become a significant challenge. These systems were designed and built with accuracy and reliability in mind, and over the years they have effectively supported business operations. However, their limitations have become more evident as business needs have evolved and digital transformation has accelerated. As a result, legacy systems […]

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As AI agents begin executing trades, managing risk, and interacting with markets through natural language, exchanges are quietly undergoing one of their most significant architectural shifts since the invention of the order book itself. The platforms that win the next cycle may not be the ones with the deepest order books, they may be the ones that are the most programmable, the most interoperable, and the most agent-ready. Victor Wu is building that shift from the inside at […]

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Post-training has become central to turning pretrained large language models (LLMs) into aligned and deployable systems. Recent progress spans supervised fine-tuning (SFT), preference optimization, reinforcement learning (RL), process supervision, verifier-guided methods, distillation, and multi-stage pipelines. Yet these methods are often discussed in fragmented ways, organized by labels or objective families rather than by the behavioral bottlenecks they address. This survey argues that LLM post-training is best understood as structured intervention on model behavior. We organize the field first […]

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London, UK Busha, a Nigerian SEC-licensed digital asset exchange, expands to the UK to serve families with connections across both regions   Busha selected Uphold as its platform partner thanks to experience in delivering on-chain infrastructure services in the UK   Busha, a crypto platform focused on building the future of money in Africa, has selected Uphold, the modern infrastructure provider for on-chain finance, to support the launch of its services in the UK. Busha serves more than a million […]

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The engineering of complex intelligent systems faces a fundamental challenge: theoretical frameworks for general intelligence and practical methodologies for enterprise system development have evolved along separate trajectories, creating a fragmentation that hinders progress toward unified solutions. This paper presents a comprehensive comparative analysis of three interrelated frameworks developed by the author: the Constrained Object Hierarchies (COH) 9-tuple, a mathematically rigorous formalism for modeling general intelligence; the Zoned Role-Based (ZRB) framework, a methodology for designing, implementing, and maintaining secure […]

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Spatiotemporal neural dynamics and oscillatory synchronization are widely implicated in biological information processing and have been hypothesized to support flexible coordination such as feature binding. By contrast, most deep learning architectures represent and propagate information through activation values, neglecting the joint dynamics of rate and phase. In this work, we introduce Kuramoto oscillatory Phase Encoding (KoPE) as an additional, evolving phase state to Vision Transformers, incorporating a neuro-inspired synchronization mechanism to advance learning efficiency. We show that KoPE […]

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Efficient recovery of critical raw materials such as lithium from metallurgical slags requires optimized liberation of target phases during comminution. To determine effective mechanical process parameters for target phase recovery, an in-depth understanding of the characteristics of slag particles is crucial. For this purpose, modern tomography techniques, such as computed tomography (CT), can provide high-resolution 3D images of micrometer-sized slag particles. However, analysis of such CT images poses challenges, such as insufficient grayscale contrast between mineral phases and […]

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Learning rules plays a crucial role in deep learning, particularly in explainable artificial intelligence and enhancing the reasoning capabilities of large language models. While existing rule learning methods are primarily designed for symbolic data, learning rules from image data without supporting image labels and automatically inventing predicates remains a challenge. In this paper, we tackle these inductive rule learning problems from images with a framework called γILP, which provides a fully differentiable pipeline from image constant substitution to […]

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Edge computing requires safe, efficient, and generalizable online decision-making, yet existing methods suffer from reactive constraint handling, fragmented scheduling frameworks, and poor generalization. We propose PACE, a unified framework shifting from reactive remediation to proactive anticipation. PACE integrates a Proactive Constrained Policy Optimizer with preemptive penalty and constraint-aware intrinsic rewards, a Nested Index Scheduler with closed-form policies for preemptive and non-preemptive AoI minimization, and a Generalizable Multi-Objective Offloading Network with histogram encoding and masking for single-policy generalization. Experiments […]

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