Apple and Google agree to change app stores after'effective duopoly' claim Apple and Google have agreed to make changes to their app stores in the UK following an intervention from the UK markets regulator. According to the Competition and Markets Authority (CMA), the tech giants have committed to not giving preferential treatment to their own apps and will be transparent about how others are approved for sale, among other agreements. It comes seven months after the regulator said Apple and Google had an effective duopoly in the UK over their dominance in the sector. The CMA's head Sarah Cardell said the proposed commitments will boost the UK's app economy and were the first of many measures. The ability to secure immediate commitments from Apple and Google reflects the unique flexibility of the UK digital markets competition regime and offers a practical route to swiftly address the concerns we've identified, she said.

Google may be forced to make major changes in the way that people use its search engine in the UK. Google may have to change the way its search engine works in the UK, including potentially offering users the option to choose rival search services, as part of new regulation from the UK's competition authority. In a decision handed down on Friday, the Competition and Markets Authority (CMA) has designated Google Search with Strategic Market Status (SMS)--a qualifier given to companies that are considered to have "substantial and entrenched market power"--which would allow the regulator to wield more power over it. This decision follows a 10-month investigation into Google, and it is the first time that these powers, which come under the UK's new Digital Markets, Competition and Consumers Act, have been used to target a major tech company. Google's SMS will last up to five years under this legislation.

The CMA has proposed ensuring fair ranking of search results on Google and providing more control for publishers over how their content is used. The CMA has proposed ensuring fair ranking of search results on Google and providing more control for publishers over how their content is used. CMA puts Google under tighter regulation with'strategic market status' designation and can enforce changes Fri 10 Oct 2025 06.53 EDTLast modified on Fri 10 Oct 2025 07.47 EDT Google faces enforced changes to its UK search business after the competition watchdog conferred a special status on the company that puts it under tighter regulation. The Competition and Market Authority (CMA) confirmed that Google has "strategic market status" (SMS) in search and search advertising, a term that means the company has such market power that it requires a special regulatory regime. The watchdog now has the power under new digital laws to order changes to how Google operates in those areas.

Compressed video action recognition has recently drawn growing attention, since it remarkably reduces the storage and computational cost via replacing raw videos by sparsely sampled RGB frames and compressed motion cues ( e.g., motion vectors and residuals). However, this task severely suffers from the coarse and noisy dynamics and the insufficient fusion of the heterogeneous RGB and motion modalities. To address the two issues above, this paper proposes a novel framework, namely Attentive Cross-modal Interaction Network with Motion Enhancement (MEACI-Net). It follows the two-stream architecture, i.e. one for the RGB modality and the other for the motion modality. Particularly, the motion stream employs a multi-scale block embedded with a denoising module to enhance representation learning. The interaction between the two streams is then strengthened by introducing the Selective Motion Complement (SMC) and Cross-Modality Augment (CMA) modules, where SMC complements the RGB modality with spatio-temporally attentive local motion features and CMA further combines the two modalities with selective feature augmentation. Extensive experiments on the UCF-101, HMDB-51 and Kinetics-400 benchmarks demonstrate the effectiveness and efficiency of MEACI-Net.

While hate speech detection (HSD) has been extensively studied in text, existing multi-modal approaches remain limited, particularly in videos. As modalities are not always individually informative, simple fusion methods fail to fully capture inter-modal dependencies. Moreover, previous work often omits relevant modalities such as on-screen text and audio, which may contain subtle hateful content and thus provide essential cues, both individually and in combination with others. In this paper, we present MM-HSD, a multi-modal model for HSD in videos that integrates video frames, audio, and text derived from speech transcripts and from frames (i.e.~on-screen text) together with features extracted by Cross-Modal Attention (CMA). We are the first to use CMA as an early feature extractor for HSD in videos, to systematically compare query/key configurations, and to evaluate the interactions between different modalities in the CMA block. Our approach leads to improved performance when on-screen text is used as a query and the rest of the modalities serve as a key. Experiments on the HateMM dataset show that MM-HSD outperforms state-of-the-art methods on M-F1 score (0.874), using concatenation of transcript, audio, video, on-screen text, and CMA for feature extraction on raw embeddings of the modalities. The code is available at https://github.com/idiap/mm-hsd

Domain generalization (DG) seeks to develop models that generalize well to unseen target domains, addressing the prevalent issue of distribution shifts in real-world applications. One line of research in DG focuses on aligning domain-level gradients and Hessians to enhance generalization. However, existing methods are computationally inefficient and the underlying principles of these approaches are not well understood. In this paper, we develop the theory of moment alignment for DG. Grounded in \textit{transfer measure}, a principled framework for quantifying generalizability between two domains, we first extend the definition of transfer measure to domain generalization that includes multiple source domains and establish a target error bound. Then, we prove that aligning derivatives across domains improves transfer measure both when the feature extractor induces an invariant optimal predictor across domains and when it does not. Notably, moment alignment provides a unifying understanding of Invariant Risk Minimization, gradient matching, and Hessian matching, three previously disconnected approaches to DG. We further connect feature moments and derivatives of the classifier head, and establish the duality between feature learning and classifier fitting. Building upon our theory, we introduce \textbf{C}losed-Form \textbf{M}oment \textbf{A}lignment (CMA), a novel DG algorithm that aligns domain-level gradients and Hessians in closed-form. Our method overcomes the computational inefficiencies of existing gradient and Hessian-based techniques by eliminating the need for repeated backpropagation or sampling-based Hessian estimation. We validate the efficacy of our approach through two sets of experiments: linear probing and full fine-tuning. CMA demonstrates superior performance in both settings compared to Empirical Risk Minimization and state-of-the-art algorithms.

Incorporating additional sensory modalities such as tactile and audio into foundational robotic models poses significant challenges due to the curse of dimensionality. This work addresses this issue through modality selection. We propose a cross-modality attention (CMA) mechanism to identify and selectively utilize the modalities that are most informative for action generation at each timestep. Furthermore, we extend the application of CMA to segment primitive skills from expert demonstrations and leverage this segmentation to train a hierarchical policy capable of solving long-horizon, contact-rich manipulation tasks.

Clustered Moore automata (CMA) are subsequen-tial Moore transducers whose states can contain smaller CMA that operate on a faster timescale, subject to an Artinian limitation.

Critics though say the decision is linked to the changed political environment the CMA is now operating in. The government has instructed the country's regulators to suggest ways of stimulating economic growth. In January, the government removed the then chair of the CMA, Marcus Bokkerink, because it was unhappy with his response to that call. He was replaced on an interim basis by Doug Gurr, former boss of Amazon UK. "The CMA has sat on this decision for over a year, yet within just a few weeks of a former Amazon boss being installed as chair, it has decided everything was absolutely fine all along, nothing to see here," said Foxglove co-executive director Rosa Curling. "This is a bad sign that Big Tech has successfully convinced the prime minister to defang our competition regulator and let Big Tech gobble up the current generation of cutting-edge tech – just like they did the last one," she told the BBC.