The jury previously heard that when questioned by police, Kadolski said he would not be able to pin someone down as he had been "sexually abused as a child". He told officers when interviewed: "I'm not the best with empathy or sympathy." Michael Cohen, for the defendant, said "others are representing Mr Kadolski in an application for permission to appeal" against the convictions. The East of England Ambulance Service said Kadolski was "immediately" suspended when it was alerted of his arrest. A spokesperson said: "We are appalled at the crimes that Jamie Kadolski has been sentenced for today. "Our thoughts are with the victims and all those affected by these horrific crimes.

The burgeoning presence of multimodal content-sharing platforms propels the development of personalized recommender systems. Previous works usually suffer from data sparsity and cold-start problems, and may fail to adequately explore semantic user-product associations from multimodal data. To address these issues, we propose a novel Multi-Modal Hypergraph Contrastive Learning (MMHCL) framework for user recommendation. For a comprehensive information exploration from user-product relations, we construct two hypergraphs, i.e. a user-to-user (u2u) hypergraph and an item-to-item (i2i) hypergraph, to mine shared preferences among users and intricate multimodal semantic resemblance among items, respectively. This process yields denser second-order semantics that are fused with first-order user-item interaction as complementary to alleviate the data sparsity issue. Then, we design a contrastive feature enhancement paradigm by applying synergistic contrastive learning. By maximizing/minimizing the mutual information between second-order (e.g. shared preference pattern for users) and first-order (information of selected items for users) embeddings of the same/different users and items, the feature distinguishability can be effectively enhanced. Compared with using sparse primary user-item interaction only, our MMHCL obtains denser second-order hypergraphs and excavates more abundant shared attributes to explore the user-product associations, which to a certain extent alleviates the problems of data sparsity and cold-start. Extensive experiments have comprehensively demonstrated the effectiveness of our method. Our code is publicly available at: https://github.com/Xu107/MMHCL.

Recommender systems (RS) have become essential in filtering information and personalizing content for users. RS techniques have traditionally relied on modeling interactions between users and items as well as the features of content using models specific to each task. The emergence of foundation models (FMs), large scale models trained on vast amounts of data such as GPT, LLaMA and CLIP, is reshaping the recommendation paradigm. This survey provides a comprehensive overview of the Foundation Models for Recommender Systems (FM4RecSys), covering their integration in three paradigms: (1) Feature-Based augmentation of representations, (2) Generative recommendation approaches, and (3) Agentic interactive systems. We first review the data foundations of RS, from traditional explicit or implicit feedback to multimodal content sources. We then introduce FMs and their capabilities for representation learning, natural language understanding, and multi-modal reasoning in RS contexts. The core of the survey discusses how FMs enhance RS under different paradigms. Afterward, we examine FM applications in various recommendation tasks. Through an analysis of recent research, we highlight key opportunities that have been realized as well as challenges encountered. Finally, we outline open research directions and technical challenges for next-generation FM4RecSys. This survey not only reviews the state-of-the-art methods but also provides a critical analysis of the trade-offs among the feature-based, the generative, and the agentic paradigms, outlining key open issues and future research directions.

Multi-modal recommender systems (MRSs) have achieved notable success in improving personalization by leveraging diverse modalities such as images, text, and audio. However, two key challenges remain insufficiently addressed: (1) Insufficient consideration of missing modality scenarios and (2) the overlooking of unique characteristics of modality features. These challenges result in significant performance degradation in realistic situations where modalities are missing. To address these issues, we propose Disentangling and Generating Modality Recommender (DGMRec), a novel framework tailored for missing modality scenarios. DGMRec disentangles modality features into general and specific modality features from an information-based perspective, enabling richer representations for recommendation. Building on this, it generates missing modality features by integrating aligned features from other modalities and leveraging user modality preferences. Extensive experiments show that DGMRec consistently outperforms state-of-the-art MRSs in challenging scenarios, including missing modalities and new item settings as well as diverse missing ratios and varying levels of missing modalities. Moreover, DGMRec's generation-based approach enables cross-modal retrieval, a task inapplicable for existing MRSs, highlighting its adaptability and potential for real-world applications. Our code is available at https://github.com/ptkjw1997/DGMRec.

Hashtag recommendation systems have emerged as a key tool for automatically suggesting relevant hashtags and enhancing content categorization and search. However, existing static models struggle to adapt to the highly dynamic nature of social media conversations, where new hashtags constantly emerge and existing ones undergo semantic shifts. To address these challenges, this paper introduces H-ADAPTS (Hashtag recommendAtion by Detecting and adAPting to Trend Shifts), a dynamic hashtag recommendation methodology that employs a trend-aware mechanism to detect shifts in hashtag usage-reflecting evolving trends and topics within social media conversations-and triggers efficient model adaptation based on a (small) set of recent posts. Additionally, the Apache Storm framework is leveraged to support scalable and fault-tolerant analysis of high-velocity social data, enabling the timely detection of trend shifts. Experimental results from two real-world case studies, including the COVID-19 pandemic and the 2020 US presidential election, demonstrate the effectiveness of H-ADAPTS in providing timely and relevant hashtag recommendations by adapting to emerging trends, significantly outperforming existing solutions.

Leviton, one of the biggest electrical component manufacturers in the world, makes high-quality products and offers a comprehensive collection of Z-Wave-compatible devices in addition to this Z-Wave 800 dimmer and switch. Smart lighting controls that operate over Wi-Fi are great, because they don't require a hub; they connect directly to your router. The downside is that they must compete with all the other clients on your home network: Your computers, gaming consoles, media streamers, smart speakers, home security cameras, smart plugs, and many, many more. I live in a very small home--less than 800 square feet--but there are still more than 80 devices connected to the Eero 6 router in my Ring Alarm Pro. Given that the Eero 6's practical limit is 128 clients, there just isn't a lot of room for light switches and dimmers.

If you're using an Android phone, you've probably noticed that Google's Gemini AI assistant seems to be popping up everywhere, the same way it's been popping into Google Search, Docs, YouTube, etc. And this is true even if you aren't using a Google-branded phone. Turns out, that's no accident because Google is paying Samsung loads of money to make sure Gemini is front and center on its phones. The information comes from a predictable source: testimony in the ongoing and potentially disastrous Google antitrust case. Google has lost two separate antitrust cases brought by the US federal government in the last year.)

The conventional cloud-based large model learning framework is increasingly constrained by latency, cost, personalization, and privacy concerns. In this survey, we explore an emerging paradigm: collaborative learning between on-device small model and cloud-based large model, which promises low-latency, cost-efficient, and personalized intelligent services while preserving user privacy. We provide a comprehensive review across hardware, system, algorithm, and application layers. At each layer, we summarize key problems and recent advances from both academia and industry. In particular, we categorize collaboration algorithms into data-based, feature-based, and parameter-based frameworks. We also review publicly available datasets and evaluation metrics with user-level or device-level consideration tailored to collaborative learning settings. We further highlight real-world deployments, ranging from recommender systems and mobile livestreaming to personal intelligent assistants. We finally point out open research directions to guide future development in this rapidly evolving field.

Reolink has become the first security camera manufacturer to obtain Works With Home Assistant certification for its Wi-Fi home security cameras. This means Reolink's cameras--not including its 4G models--can now process video feeds, AI alerts, and device controls entirely within users' home networks to enhance user privacy. Home Assistant is a free and open-source smart home software platform managed by the Open Home Foundation. It has been embraced by many DIY smart home enthusiasts, and it can run on lots of different hardware, ranging from Raspberry Pi and Arm processors to the 64-bit x86 architecture commonly found in Mini PCs. It can even operate as a virtual machine on a laptop or desktop running MacOS or Windows.

Dear Prudence is Slate's advice column. I was browsing a men-seeking-men dating app when I came across the profile of my neighbor, "Gary." He described himself as "single and looking for fun." I happen to know that Gary is married with two kids under 3 years old. The thing is, I don't know his wife "Bethany" that well; we've only ever waved to one another in the neighborhood and briefly engaged in small talk when we run into each other.