As shown in Figure 1(b) and 1(c), when compressing the generator, the loss of the discriminator gradually tends to zero, such asituation indicates that the capacity of the discriminator significantly surpasses that of the lightweight generator.

Artificial intelligence (AI) is rapidly transforming public - sector processes worldwide, yet standardized measures rarely address the unique drivers, governance models, and cultural nuances of the Gulf Cooperation Council (GCC) countries. This study employs a theory - driven foundation derived from an in - depth analysis of literature review and six National AI Strategies (NASs), coupled with a data - driven approach that utilizes a survey of 203 mid - and senior - level government employees and advanced statistical techniques (K - Means clustering, Principal Component Analysis, and Partial Least Squares Structural Equation Modeling). By combining policy insights with empirical evidence, the research develops and validates a novel AI Adoption Index specifically tailored to the GCC public sector. Findings indicate that robust technical infrastructure and clear policy mandates exert the strongest influence on successful AI implementations, overshadowing organizational readiness in early adoption stages. The combined model explains 70% of the variance in AI outcomes, suggesting that resource - rich environments and top - down policy directives can drive rapid but uneven technology uptake. By consolidating key dimensions (Technical Infrastructure (TI), Organizational Readiness (O R), and Governance Environment (GE)) into a single composite index, this study provides a holistic yet context - sensitive tool for benchmarking AI maturity. The index offers actionable guidance for policymakers seeking to harmonize large - scale deployments w ith ethical and regulatory standards. Beyond advancing academic discourse, these insights inform more strategic allocation of resources, cross - country cooperation, and capacity - building initiatives, thereby supporting sustained AI - driven transformation in the GCC region and beyond.

The rapid expansion of artificial intelligence (AI) in the Gulf Cooperation Council (GCC) raises a central question: are investments in compute infrastructure matched by an equally robust build-out of skills, incentives, and governance? Grounded in socio-technical systems (STS) theory, this mixed-methods study audits workforce preparedness across Kingdom of Saudi Arabia (KSA), the United Arab Emirates (UAE), Qatar, Kuwait, Bahrain, and Oman. We combine term frequency--inverse document frequency (TF--IDF) analysis of six national AI strategies (NASs), an inventory of 47 publicly disclosed AI initiatives (January 2017--April 2025), paired case studies, the Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) and the Saudi Data & Artificial Intelligence Authority (SDAIA) Academy, and a scenario matrix linking oil-revenue slack (technical capacity) to regulatory coherence (social alignment). Across the corpus, 34/47 initiatives (0.72; 95% Wilson CI 0.58--0.83) exhibit joint social--technical design; country-level indices span 0.57--0.90 (small n; intervals overlap). Scenario results suggest that, under our modeled conditions, regulatory convergence plausibly binds outcomes more than fiscal capacity: fragmented rules can offset high oil revenues, while harmonized standards help preserve progress under austerity. We also identify an emerging two-track talent system, research elites versus rapidly trained practitioners, that risks labor-market bifurcation without bridging mechanisms. By extending STS inquiry to oil-rich, state-led economies, the study refines theory and sets a research agenda focused on longitudinal coupling metrics, ethnographies of coordination, and outcome-based performance indicators.

Deep vision models have achieved remarkable classification performance by leveraging a hierarchical architecture in which human-interpretable concepts emerge through the composition of individual neurons across layers. Given the distributed nature of representations, pinpointing where specific visual concepts are encoded within a model remains a crucial yet challenging task. In this paper, we introduce an effective circuit discovery method, called Granular Concept Circuit (GCC), in which each circuit represents a concept relevant to a given query. To construct each circuit, our method iteratively assesses inter-neuron connectivity, focusing on both functional dependencies and semantic alignment. By automatically discovering multiple circuits, each capturing specific concepts within that query, our approach offers a profound, concept-wise interpretation of models and is the first to identify circuits tied to specific visual concepts at a fine-grained level. We validate the versatility and effectiveness of GCCs across various deep image classification models.

Nowadays, with the advancement of deep neural networks (DNNs) and the availability of large-scale datasets, the face recognition (FR) model has achieved exceptional performance. However, since the parameter magnitude of the fully connected (FC) layer directly depends on the number of identities in the dataset. If training the FR model on large-scale datasets, the size of the model parameter will be excessively huge, leading to substantial demand for computational resources, such as time and memory. This paper proposes the attention fully connected (AttFC) layer, which could significantly reduce computational resources. AttFC employs an attention loader to generate the generative class center (GCC), and dynamically store the class center with Dynamic Class Container (DCC). DCC only stores a small subset of all class centers in FC, thus its parameter count is substantially less than the FC layer. Also, training face recognition models on large-scale datasets with one GPU often encounter out-of-memory (OOM) issues. AttFC overcomes this and achieves comparable performance to state-of-the-art methods.

Learning high-performance deep neural networks for dynamic modeling of high Degree-Of-Freedom (DOF) robots remains challenging due to the sampling complexity. Typical unknown system disturbance caused by unmodeled dynamics (such as internal compliance, cables) further exacerbates the problem. In this paper, a novel framework characterized by both high data efficiency and disturbance-adapting capability is proposed to address the problem of modeling gravitational dynamics using deep nets in feedforward gravity compensation control for high-DOF master manipulators with unknown disturbance. In particular, Feedforward Deep Neural Networks (FDNNs) are learned from both prior knowledge of an existing analytical model and observation of the robot system by Knowledge Distillation (KD). Through extensive experiments in high-DOF master manipulators with significant disturbance, we show that our method surpasses a standard Learning-from-Scratch (LfS) approach in terms of data efficiency and disturbance adaptation. Our initial feasibility study has demonstrated the potential of outperforming the analytical teacher model as the training data increases.

Recent works show that mean-teaching is an effective framework for unsupervised domain adaptive person re-identification. However, existing methods perform contrastive learning on selected samples between teacher and student networks, which is sensitive to noises in pseudo labels and neglects the relationship among most samples. Moreover, these methods are not effective in cooperation of different teacher networks. To handle these issues, this paper proposes a Graph Consistency based Mean-Teaching (GCMT) method with constructing the Graph Consistency Constraint (GCC) between teacher and student networks. Specifically, given unlabeled training images, we apply teacher networks to extract corresponding features and further construct a teacher graph for each teacher network to describe the similarity relationships among training images. To boost the representation learning, different teacher graphs are fused to provide the supervise signal for optimizing student networks. GCMT fuses similarity relationships predicted by different teacher networks as supervision and effectively optimizes student networks with more sample relationships involved. Experiments on three datasets, i.e., Market-1501, DukeMTMCreID, and MSMT17, show that proposed GCMT outperforms state-of-the-art methods by clear margin. Specially, GCMT even outperforms the previous method that uses a deeper backbone. Experimental results also show that GCMT can effectively boost the performance with multiple teacher and student networks. Our code is available at https://github.com/liu-xb/GCMT .

ANSR, a company that establishes and operates global capability centres (GCCs) for large enterprises, has announced the acquisition of FastNext in an all-stock deal. An AI-driven tech company, FastNext was incubated by CoffeeBeans, a boutique product and technology services firm, and has over 20 employees. ANSR said the acquisition will enable it to help its clients build their GCC teams using deep learning and AI-powered tools across talent acquisition and management, smart workspace management and business workflows. "Organisations aren't going digital, they are digital. Building global capability and engineering centres for technology innovation is a priority for multiple Fortune 1000 companies. We are excited to integrate FastNext's portfolio of AI-based tools and solutions into our portfolio so that data is at the core of everything that our customers do to win in the digital age," said Lalit Ahuja, founder and CEO, ANSR.

Graph representation learning has emerged as a powerful technique for addressing real-world problems. Various downstream graph learning tasks have benefited from its recent developments, such as node classification, similarity search, and graph classification. However, prior arts on graph representation learning focus on domain specific problems and train a dedicated model for each graph dataset, which is usually non-transferable to out-of-domain data. Inspired by the recent advances in pre-training from natural language processing and computer vision, we design Graph Contrastive Coding (GCC) -- a self-supervised graph neural network pre-training framework -- to capture the universal network topological properties across multiple networks. We design GCC's pre-training task as subgraph instance discrimination in and across networks and leverage contrastive learning to empower graph neural networks to learn the intrinsic and transferable structural representations. We conduct extensive experiments on three graph learning tasks and ten graph datasets. The results show that GCC pre-trained on a collection of diverse datasets can achieve competitive or better performance to its task-specific and trained-from-scratch counterparts. This suggests that the pre-training and fine-tuning paradigm presents great potential for graph representation learning.

AntWorks, a global provider of artificial intelligence and intelligent automation solutions powered by fractal science, today announced an exclusive partnership with the SEED Group, a member of The Private Office of Sheikh Saeed bin Ahmed Al Maktoum. The partnership will support expansion of intelligent automation within the Middle East (ME), a region where AI is expected to become a US$320 billion by 2030. The SEED Group establishes groundbreaking companies with a strong presence in the Gulf Cooperation Council (GCC) and will work with AntWorks to offer ethical AI solutions for GCC companies with ANTsteinTM SQUARE, the world's first and only Integrated Automation Platform (IAP), powered by fractal science. AntWorks seek to replicate its success across Asia, the UK and US, where the organisation has automated entire business processes end-to-end for many clients across the BFSI (Banking, Financial Services and Insurance), transportation, logistics and public sector, among others. With successful adoption of AntWorks' IAP solution, businesses will stand to save millions and realise increased performance and efficiency by automating and processing business data, including unstructured data, which will make up 80% of the world's data by 2025.