In Sex Reviews, writers offer a sober critical assessment of the sex scenes in new films and television series. This installment contains spoilers for Babygirl. Nestled amid a nice little set of Christmas releases is Babygirl, an erotic thriller set during the holidays, written and directed by Halina Reijn of Bodies, Bodies, Bodies fame. The film stars Nicole Kidman as Romy, a work-addicted CEO of a robotics company, who lives with her play-directing, gray-goatee-sporting husband Jacob (Antonio Banderas) and two teenage daughters in a gorgeous Manhattan apartment. Romy's creeping dissatisfaction with the rounds of Botox, therapy, and meetings that make up her life comes to a head when she meets Samuel, an intern at her company, played by hyper-handsome English actor Harris Dickinson. In fits and starts, the Gen X Romy and Gen Z Samuel discover that they have a very particular type of chemistry: She wants to be told what to do, and he's willing to tell her.

Whether a large and fuzzy social media sensation or deep-sea slug slunking around the ocean's Midnight Zone, there are still so many exciting animals on Earth just waiting for their close-up. In that spirit, here are the 11 of the most exciting animal stories that Popular Science covered this year. A wildlife filmmaker and biology doctoral student took what could be the first picture of a newborn great white shark. Filmmaker Carlos Gauna and University of California, Riverside biology doctoral student Phillip Sternes were looking for sharks near Santa Barbara on California's central coast. Most great whites are gray on top with white bellies, but Gauana's drone camera showed a roughly 5-foot-long shark pup that had more white on its body than normal.

This was the year that gen Z had their "Brat summer", or so we were led to believe. Inspired by the hit album by pop sensation Charli xcx, the trend was seen to embody all the messiness of modern youth: trashy, chaotic and bright green. But on the teenager's social media platform of choice, TikTok, a more sepia music trend has been taking root. Despite having an endless amount of music to pair with their short, scrollable videos, TikTok users have been raiding the back catalogues of artists from yesteryear including Bronski Beat and Sade to soundtrack their posts. This year set a new high for use of old tracks on British TikTok posts, with tunes more than five years old accounting for 19 out of its 50 top tracks this year.

Handwritten document recognition (HDR) is one of the most challenging tasks in the field of computer vision, due to the various writing styles and complex layouts inherent in handwritten texts. Traditionally, this problem has been approached as two separate tasks, handwritten text recognition and layout analysis, and struggled to integrate the two processes effectively. This paper introduces HAND (Hierarchical Attention Network for Multi-Scale Document), a novel end-to-end and segmentation-free architecture for simultaneous text recognition and layout analysis tasks. Our model's key components include an advanced convolutional encoder integrating Gated Depth-wise Separable and Octave Convolutions for robust feature extraction, a Multi-Scale Adaptive Processing (MSAP) framework that dynamically adjusts to document complexity and a hierarchical attention decoder with memory-augmented and sparse attention mechanisms. These components enable our model to scale effectively from single-line to triple-column pages while maintaining computational efficiency. Additionally, HAND adopts curriculum learning across five complexity levels. To improve the recognition accuracy of complex ancient manuscripts, we fine-tune and integrate a Domain-Adaptive Pre-trained mT5 model for post-processing refinement. Extensive evaluations on the READ 2016 dataset demonstrate the superior performance of HAND, achieving up to 59.8% reduction in CER for line-level recognition and 31.2% for page-level recognition compared to state-of-the-art methods. The model also maintains a compact size of 5.60M parameters while establishing new benchmarks in both text recognition and layout analysis. Source code and pre-trained models are available at : https://github.com/MHHamdan/HAND.

What is appropriateness? Humans navigate a multi-scale mosaic of interlocking notions of what is appropriate for different situations. We act one way with our friends, another with our family, and yet another in the office. Likewise for AI, appropriate behavior for a comedy-writing assistant is not the same as appropriate behavior for a customer-service representative. What determines which actions are appropriate in which contexts? And what causes these standards to change over time? Since all judgments of AI appropriateness are ultimately made by humans, we need to understand how appropriateness guides human decision making in order to properly evaluate AI decision making and improve it. This paper presents a theory of appropriateness: how it functions in human society, how it may be implemented in the brain, and what it means for responsible deployment of generative AI technology.

As working with large datasets becomes standard, the task of accurately retrieving images containing objects of interest by an open set textual query gains practical importance. The current leading approach utilizes a pre-trained CLIP model without any adaptation to the target domain, balancing accuracy and efficiency through additional post-processing. In this work, we propose FOR: Finetuning for Object-centric Open-vocabulary Image Retrieval, which allows finetuning on a target dataset using closed-set labels while keeping the visual-language association crucial for open vocabulary retrieval. FOR is based on two design elements: a specialized decoder variant of the CLIP head customized for the intended task, and its coupling within a multi-objective training framework. Together, these design choices result in a significant increase in accuracy, showcasing improvements of up to 8 mAP@50 points over SoTA across three datasets. Additionally, we demonstrate that FOR is also effective in a semi-supervised setting, achieving impressive results even when only a small portion of the dataset is labeled.

Ladies and gentleman,

PCWorld executive editor Gordon Mah Ung, a tireless journalist we once described as a founding father of hardcore tech journalism, passed away over the weekend after a hard-fought battle with pancreatic cancer. Gordon was 58, and leaves behind a loving wife, two children, older sister, and mother. With more than 25 years' experience covering computer tech broadly and computer chips specifically, Gordon's dogged reporting, one-of-a-kind personality, and commitment to journalistic standards touched many, many lives. He will be profoundly missed by co-workers, industry sources, and the PC enthusiasts who read his words and followed him as a video creator. Gordon studied journalism at San Francisco State University and then worked as a police reporter for the Contra Costa Times in the late 1990s. In 1997, he joined Computerworld (a PCWorld sister publication) before I recruited him to join boot magazine (later re-launched as Maximum PC), where he would ultimately lead hardware coverage for 16 years. At Maximum PC, Gordon developed his trademark voice that blended a hardcore passion for PC tech with non-sequiturs, deadpan humor, and occasional bursts of outrage.

Singing voice synthesis (SVS) system is expected to generate high-fidelity singing voice from given music scores (lyrics, duration and pitch). Recently, diffusion models have performed well in this field. However, sacrificing inference speed to exchange with high-quality sample generation limits its application scenarios. In order to obtain high quality synthetic singing voice more efficiently, we propose a singing voice synthesis method based on the consistency model, ConSinger, to achieve high-fidelity singing voice synthesis with minimal steps. The model is trained by applying consistency constraint and the generation quality is greatly improved at the expense of a small amount of inference speed. Our experiments show that ConSinger is highly competitive with the baseline model in terms of generation speed and quality. Audio samples are available at https://keylxiao.github.io/consinger.

Retrieval from graph data is crucial for augmenting large language models (LLM) with both open-domain knowledge and private enterprise data, and it is also a key component in the recent GraphRAG system (edge et al., 2024). Despite decades of research on knowledge graphs and knowledge base question answering, leading LLM frameworks (e.g. Langchain and LlamaIndex) have only minimal support for retrieval from modern encyclopedic knowledge graphs like Wikidata. In this paper, we analyze the root cause and suggest that modern RDF knowledge graphs (e.g. Wikidata, Freebase) are less efficient for LLMs due to overly large schemas that far exceed the typical LLM context window, use of resource identifiers, overlapping relation types and lack of normalization. As a solution, we propose property graph views on top of the underlying RDF graph that can be efficiently queried by LLMs using Cypher. We instantiated this idea on Wikidata and introduced CypherBench, the first benchmark with 11 large-scale, multi-domain property graphs with 7.8 million entities and over 10,000 questions. To achieve this, we tackled several key challenges, including developing an RDF-to-property graph conversion engine, creating a systematic pipeline for text-to-Cypher task generation, and designing new evaluation metrics.