Tensor decompositions play a crucial role in numerous applications related to multi-way data analysis. By employing a Bayesian framework with sparsity-inducing priors, Bayesian Tensor Ring (BTR) factorization offers probabilistic estimates and an effective approach for automatically adapting the tensor ring rank during the learning process. However, previous BTR method employs an Automatic Relevance Determination (ARD) prior, which can lead to sub-optimal solutions. Besides, it solely focuses on continuous data, whereas many applications involve discrete data. More importantly, it relies on the Coordinate-Ascent Variational Inference (CAVI) algorithm, which is inadequate for handling large tensors with extensive observations. These limitations greatly limit its application scales and scopes, making it suitable only for small-scale problems, such as image/video completion. To address these issues, we propose a novel BTR model that incorporates a nonparametric Multiplicative Gamma Process (MGP) prior, known for its superior accuracy in identifying latent structures. To handle discrete data, we introduce the P\'olya-Gamma augmentation for closed-form updates. Furthermore, we develop an efficient Gibbs sampler for consistent posterior simulation, which reduces the computational complexity of previous VI algorithm by two orders, and an online EM algorithm that is scalable to extremely large tensors. To showcase the advantages of our model, we conduct extensive experiments on both simulation data and real-world applications.

Despite fears that artificial intelligence (AI) could influence the outcome of elections around the world, the United States technology giant Meta said it detected little impact across its platforms this year. That was in part due to defensive measures designed to prevent coordinated networks of accounts, or bots, from grabbing attention on Facebook, Instagram and Threads, Meta president of global affairs Nick Clegg told reporters on Tuesday. "I don't think the use of generative AI was a particularly effective tool for them to evade our trip wires," Clegg said of actors behind coordinated disinformation campaigns. In 2024, Meta says it ran several election operations centres around the world to monitor content issues, including during elections in the US, Bangladesh, Brazil, France, India, Indonesia, Mexico, Pakistan, South Africa, the United Kingdom and the European Union. Most of the covert influence operations it has disrupted in recent years were carried out by actors from Russia, Iran and China, Clegg said, adding that Meta took down about 20 "covert influence operations" on its platform this year.

Miliaku Nwabueze, a senior program manager at Code for Science & Society, had been concerned for some time about the role of technology in state violence. Then, on 7 October of last year, Hamas entered Israel, killing and kidnapping about 1,400 people. Less than a week later, as Israel ordered 1.1 million Palestinians out of northern Gaza in the onset of its deadly retaliation, Nwabueze decided to write a message to her colleagues on the US-based non-profit organization's Slack channel. "Hey y'all … I have been watching multiple genocides around the world," she began, naming Palestine as well as Sudan, the Congo and Artsakh. "All of these have heavy linkages to the tech industry." The 30-year-old went on to assert that CS&S – whose stated mission is to "advance the power of data to improve the social and economic lives of all people" – should say, at the minimum, "we support demands for a ceasefire" in Gaza.

Meta has intervened to take down about 20 covert influence operations around the world this year, it has emerged – though the tech firm said fears of AI-fuelled fakery warping elections had not materialised in 2024. Nick Clegg, the president of global affairs at the company that runs Facebook, Instagram and WhatsApp, said Russia was still the No 1 source of the adversarial online activity but said in a briefing it was "striking" how little AI was used to try to trick voters in the busiest ever year for elections around the world. The former British deputy prime minister revealed that Meta, which has more than 3 billion users, had to take down just over 500,000 requests to generate images on its own AI tools of Donald Trump and Kamala Harris, JD Vance and Joe Biden in the month leading up to US election day. But the firm's security experts had to tackle a new operation using fake accounts to manipulate public debate for a strategic goal at the rate of more than one every three weeks. The "coordinated inauthentic behaviour" incidents included a Russian network using dozens of Facebook accounts and fictitious news websites to target people in Georgia, Armenia and Azerbaijan.

AI-generated content played a much smaller role in global election misinformation than what many officials and researchers had feared, according to a new analysis from Meta. In an update on its efforts to safeguard dozens of elections in 2024, the company said that AI content made up only a fraction of election-related misinformation that was caught and labeled by its fact checkers. "During the election period in the major elections listed above, ratings on AI content related to elections, politics and social topics represented less than 1% of all fact-checked misinformation," the company shared in a blog post, referring to elections in the US, UK, Bangladesh, Indonesia, India, Pakistan, France, South Africa, Mexico and Brazil, as well as the EU's Parliamentary elections. The update comes after numerous government officials and researchers for months raised the alarm about the role generative AI could play in supercharging election misinformation in a year when more than 2 billion people were expected to go to the polls. But those fears largely did not play out -- at least on Meta's platforms -- according to the company's President of Global Affairs, Nick Clegg.

We propose a methodology that exploits large and diverse data sets to accurately estimate the ambient medium's Green's functions in strongly scattering media. Given these estimates, obtained with and without the use of neural networks, excellent imaging results are achieved, with a resolution that is better than that of a homogeneous medium. This phenomenon, also known as super-resolution, occurs because the ambient scattering medium effectively enhances the physical imaging aperture. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.

Effective prompting of generative AI is challenging for many users, particularly in expressing context for comprehension tasks such as explaining spreadsheet formulas, Python code, and text passages. Prompt middleware aims to address this barrier by assisting in prompt construction, but barriers remain for users in expressing adequate control so that they can receive AI-responses that match their preferences. We conduct a formative survey (n=38) investigating user needs for control over AI-generated explanations in comprehension tasks, which uncovers a trade-off between standardized but predictable support for prompting, and adaptive but unpredictable support tailored to the user and task. To explore this trade-off, we implement two prompt middleware approaches: Dynamic Prompt Refinement Control (Dynamic PRC) and Static Prompt Refinement Control (Static PRC). The Dynamic PRC approach generates context-specific UI elements that provide prompt refinements based on the user's prompt and user needs from the AI, while the Static PRC approach offers a preset list of generally applicable refinements. We evaluate these two approaches with a controlled user study (n=16) to assess the impact of these approaches on user control of AI responses for crafting better explanations. Results show a preference for the Dynamic PRC approach as it afforded more control, lowered barriers to providing context, and encouraged exploration and reflection of the tasks, but that reasoning about the effects of different generated controls on the final output remains challenging. Drawing on participant feedback, we discuss design implications for future Dynamic PRC systems that enhance user control of AI responses. Our findings suggest that dynamic prompt middleware can improve the user experience of generative AI workflows by affording greater control and guide users to a better AI response.

Programmable Logic Controllers (PLCs) are microcomputers essential for automating factory operations. Structured Text (ST), a high-level language adhering to the IEC 61131-3 standard, is pivotal for PLCs due to its ability to express logic succinctly and to seamlessly integrate with other languages within the same standard. However, vendors develop their own customized versions of ST, and the lack of comprehensive and standardized documentation for the full semantics of ST has contributed to inconsistencies in how the language is implemented. Consequently, the steep learning curve associated with ST, combined with ever-evolving industrial requirements, presents significant challenges for developers. In response to these issues, we present AutoPLC, an LLM-based approach designed to automate the generation of vendor-specific ST code. To facilitate effective code generation, we first built a comprehensive knowledge base, including Rq2ST Case Library (requirements and corresponding implementations) and Instruction libraries. Then we developed a retrieval module to incorporate the domain-specific knowledge by identifying pertinent cases and instructions, guiding the LLM to generate code that meets the requirements. In order to verify and improve the quality of the generated code, we designed an adaptable code checker. If errors are detected, we initiate an iterative self-improvement process to instruct the LLM to revise the generated code. We evaluate AutoPLC's performance against seven state-of-the-art baselines using three benchmarks, one for open-source basic ST and two for commercial Structured Control Language (SCL) from Siemens. The results show that our approach consistently achieves superior performance across all benchmarks. Ablation study emphasizes the significance of our modules. Further manual analysis confirm the practical utility of the ST code generated by AutoPLC.

We apply NER to a particular sub-genre of legal texts in German: the genre of legal norms regulating administrative processes in public service administration. The analysis of such texts involves identifying stretches of text that instantiate one of ten classes identified by public service administration professionals. We investigate and compare three methods for performing Named Entity Recognition (NER) to detect these classes: a Rule-based system, deep discriminative models, and a deep generative model. Our results show that Deep Discriminative models outperform both the Rule-based system as well as the Deep Generative model, the latter two roughly performing equally well, outperforming each other in different classes. The main cause for this somewhat surprising result is arguably the fact that the classes used in the analysis are semantically and syntactically heterogeneous, in contrast to the classes used in more standard NER tasks. Deep Discriminative models appear to be better equipped for dealing with this heterogenerity than both generic LLMs and human linguists designing rule-based NER systems.

Local clustering aims to find a compact cluster near the given starting instances. This work focuses on graph local clustering, which has broad applications beyond graphs because of the internal connectivities within various modalities. While most existing studies on local graph clustering adopt the discrete graph setting (i.e., unweighted graphs without self-loops), real-world graphs can be more complex. In this paper, we extend the non-approximating Andersen-Chung-Lang ("ACL") algorithm beyond discrete graphs and generalize its quadratic optimality to a wider range of graphs, including weighted, directed, and self-looped graphs and hypergraphs. Specifically, leveraging PageRank, we propose two algorithms: GeneralACL for graphs and HyperACL for hypergraphs. We theoretically prove that, under two mild conditions, both algorithms can identify a quadratically optimal local cluster in terms of conductance with at least 1/2 probability. On the property of hypergraphs, we address a fundamental gap in the literature by defining conductance for hypergraphs from the perspective of hypergraph random walks. Additionally, we provide experiments to validate our theoretical findings.