Author Simon Stålenhag has a new work out this month. December is traditionally a quieter month for new releases from publishers and that's definitely true this year, with a sparser than usual science-fiction offering to chew over. That said, there are some intriguing titles out this month, and I'm looking forward to the new book from artist and author Simon Stålenhag, another illustrated dystopia, as well as a mysterious-sounding Russian novel, and the conclusion of Bethany Jacobs's excellent space opera trilogy. Jacobs has written a piece for the New Scientist Book Club about how the late Iain M. Banks inspired her own world-building. The Book Club is currently reading Banks's classic Culture novel - do join us .

In the Swiss Alps, a plan to tidy up Romansh--spoken by less than one per cent of the country--set off a decades-long quarrel over identity, belonging, and the sound of authenticity. After reformers launched Rumantsch Grischun, a standardized version of Romansh's various dialects, traditionalists denounced it as a "bastard," a "castrated" tongue, an act of "linguistic murder." Ask him how it all began, and he remembers the ice. It was a bitter morning in January, 1982, when Bernard Cathomas, aged thirty-six, carefully picked his way up a slippery, sloping Zurich street. His destination was No. 33, an ochre house with green shutters--the home of Heinrich Schmid, a linguist at the University of Zurich. Inside, the décor suggested that "professor" was an encompassing identity: old wooden floors, a faded carpet, a living room seemingly untouched since the nineteen-thirties, when Schmid had grown up in the house. Schmid's wife served, a Swiss carrot cake that manages bourgeois indulgence with a vegetable alibi. Cathomas had already written from Chur, in the canton of the Grisons, having recently become the general secretary of the Lia Rumantscha, a small association charged with protecting Switzerland's least known national language, Romansh. Spoken by less than one per cent of the Swiss population, the language was itself splintered into five major "idioms," not always readily intelligible to one another, each with its own spelling conventions. Earlier attempts at unification had collapsed in rivalries. In his letter, Cathomas said that Schmid's authority would be valuable in standardizing the language. Cathomas wrote in German but started and ended in his native Sursilvan, the biggest of the Romansh idioms: " ." Translation: "I thank you very much for your interest and attention to this problem." Schmid, the man he was counting on, hadn't grown up speaking Romansh; he first learned it in high school, and later worked on the "Dicziunari Rumantsch Grischun," a Romansh dictionary begun in 1904 and still lumbering toward completion.

These smart bird feeders use connected cameras to capture up-close images and videos of visiting birds. We may earn revenue from the products available on this page and participate in affiliate programs. Birds are difficult to photograph. They move quickly, arrive sporadically, and have an uncanny knack for avoiding the camera. Birdfy's smart bird feeders make it easy to capture photos and videos of your feathered friends with a connected camera.

'It was extremely pornographic': Cara Hunter on the deepfake video that nearly ended her political career The Irish politician was targeted in 2022, in the final weeks of her run for office. When Cara Hunter, the Irish politician, looks back on the moment she found out she had been deepfaked, she says it is "like watching a horror movie". The setting is her grandmother's rural home in the west of Tyrone on her 90th birthday, April 2022. "Everyone was there," she says. "I was sitting with all my closest family members and family friends when I got a notification through Facebook Messenger." It was from a stranger.

Methods for query answering over incomplete knowledge graphs retrieve entities that are \emph{likely} to be answers, which is particularly useful when such answers cannot be reached by direct graph traversal due to missing edges. However, existing approaches have focused on queries formalized using first-order-logic. In practice, many real-world queries involve constraints that are inherently vague or context-dependent, such as preferences for attributes or related categories. Addressing this gap, we introduce the problem of query answering with soft constraints. We formalize the problem and introduce two efficient methods designed to adjust query answer scores by incorporating soft constraints without disrupting the original answers to a query. These methods are lightweight, requiring tuning only two parameters or a small neural network trained to capture soft constraints while maintaining the original ranking structure. To evaluate the task, we extend existing QA benchmarks by generating datasets with soft constraints. Our experiments demonstrate that our methods can capture soft constraints while maintaining robust query answering performance and adding very little overhead. With our work, we explore a new and flexible way to interact with graph databases that allows users to specify their preferences by providing examples interactively.

In this paper, we propose a novel Multi-Modal Scene Graph with Kolmogorov-Arnold Expert Network for Audio-Visual Question Answering (SHRIKE). The task aims to mimic human reasoning by extracting and fusing information from audio-visual scenes, with the main challenge being the identification of question-relevant cues from the complex audiovisual content. Existing methods fail to capture the structural information within video, and suffer from insufficient fine-grained modeling of multi-modal features. T o address these issues, we are the first to introduce a new multi-modal scene graph that explicitly models the objects and their relationship as a visually grounded, structured representation of the audio-visual scene. Furthermore, we design a Kol-mogorov-Arnold Network (KAN)-based Mixture of Experts (MoE) to enhance the expressive power of the temporal integration stage. This enables more fine-grained modeling of cross-modal interactions within the question-aware fused audio-visual representation, leading to capture richer and more nuanced patterns and then improve temporal reasoning performance. W e evaluate the model on the established MUSIC-A VQA and MUSIC-A VQA v2 benchmarks, where it achieves state-of-the-art performance. Code and model checkpoints will be publicly released.

We introduce Vision Bridge Transformer (ViBT), a large-scale instantiation of Brownian Bridge Models designed for conditional generation. Unlike traditional diffusion models that transform noise into data, Bridge Models directly model the trajectory between inputs and outputs, creating an efficient data-to-data translation paradigm. By scaling these models to 20B and 1.3B parameters, we demonstrate their effectiveness for image and video translation tasks. To support this scale, we adopt a Transformer architecture and propose a variance-stabilized velocity-matching objective for robust training. Together, these advances highlight the power of scaling Bridge Models for instruction-based image editing and complex video translation.

Abstract--This paper investigates fake news detection as a downstream evaluation of Transformer representations, bench-marking encoder-only and decoder-only pre-trained models (BERT, GPT -2, Transformer-XL) as frozen embedders paired with lightweight classifiers. Through controlled preprocessing comparing pooling versus padding and neural versus linear heads, results demonstrate that contextual self-attention encodings consistently transfer effectively. BERT embeddings combined with logistic regression outperform neural baselines on LIAR dataset splits, while analyses of sequence length and aggregation reveal robustness to truncation and advantages from simple max or average pooling. In the pre-digital era, the dissemination of information to mass audiences was predominantly controlled by established publishing organizations and media conglomerates that maintained editorial standards and fact-checking processes. The advent of the Internet and the subsequent proliferation of social media platforms have fundamentally transformed this landscape, democratizing information sharing by enabling any individual to broadcast news and content to global audiences with unprecedented speed and scale [6]. While this democratization has fostered greater accessibility to diverse perspectives, it has simultaneously introduced significant challenges to ensuring the validity, authenticity, and reliability of the information being circulated [8].

This work contributes towards balancing the inclusivity and global applicability of natural language processing techniques by proposing the first 'name entity recognition' dataset for Kurdish Sorani, a low-resource and under-represented language, that consists of 64,563 annotated tokens. It also provides a tool for facilitating this task in this and many other languages and performs a thorough comparative analysis, including classic machine learning models and neural systems. The results obtained challenge established assumptions about the advantage of neural approaches within the context of NLP. Conventional methods, in particular CRF, obtain F1-scores of 0.825, outperforming the results of BiLSTM-based models (0.706) significantly. These findings indicate that simpler and more computationally efficient classical frameworks can outperform neural architectures in low-resource settings.

Large Language models are revolutionizing the conversational recommender systems through their impressive capabilities in instruction comprehension, reasoning, and human interaction. A core factor underlying effective recommendation dialogue is the ability to infer and reason about users' mental states (such as desire, intention, and belief), a cognitive capacity commonly referred to as Theory of Mind. Despite growing interest in evaluating ToM in LLMs, current benchmarks predominantly rely on synthetic narratives inspired by Sally-Anne test, which emphasize physical perception and fail to capture the complexity of mental state inference in realistic conversational settings. Moreover, existing benchmarks often overlook a critical component of human ToM: behavioral prediction, the ability to use inferred mental states to guide strategic decision-making and select appropriate conversational actions for future interactions. To better align LLM-based ToM evaluation with human-like social reasoning, we propose RecToM, a novel benchmark for evaluating ToM abilities in recommendation dialogues. RecToM focuses on two complementary dimensions: Cognitive Inference and Behavioral Prediction. The former focus on understanding what has been communicated by inferring the underlying mental states. The latter emphasizes what should be done next, evaluating whether LLMs can leverage these inferred mental states to predict, select, and assess appropriate dialogue strategies. Extensive experiments on state-of-the-art LLMs demonstrate that RecToM poses a significant challenge. While the models exhibit partial competence in recognizing mental states, they struggle to maintain coherent, strategic ToM reasoning throughout dynamic recommendation dialogues, particularly in tracking evolving intentions and aligning conversational strategies with inferred mental states.