It's been another wild few days in Elon Musk news. Stay tuned for our coverage. In personal news, I deleted Instagram from my phone to try out a month without it there. Instead of scrolling, I've been listening to Shygirl and Lady Gaga's new music. DeepSeek roiled the US stock market last week by proposing that AI shouldn't really be all that expensive. The suggestion was so stunning it wiped about 600bn off of Nvidia's market cap in one day.

We present and release MIDI-GPT, a generative system based on the Transformer architecture that is designed for computer-assisted music composition workflows. MIDI-GPT supports the infilling of musical material at the track and bar level, and can condition generation on attributes including: instrument type, musical style, note density, polyphony level, and note duration. In order to integrate these features, we employ an alternative representation for musical material, creating a time-ordered sequence of musical events for each track and concatenating several tracks into a single sequence, rather than using a single time-ordered sequence where the musical events corresponding to different tracks are interleaved. We also propose a variation of our representation allowing for expressiveness. We present experimental results that demonstrate that MIDI-GPT is able to consistently avoid duplicating the musical material it was trained on, generate music that is stylistically similar to the training dataset, and that attribute controls allow enforcing various constraints on the generated material. We also outline several real-world applications of MIDI-GPT, including collaborations with industry partners that explore the integration and evaluation of MIDI-GPT into commercial products, as well as several artistic works produced using it.

Video personalization, which generates customized videos using reference images, has gained significant attention. However, prior methods typically focus on single-concept personalization, limiting broader applications that require multi-concept integration. Attempts to extend these models to multiple concepts often lead to identity blending, which results in composite characters with fused attributes from multiple sources. This challenge arises due to the lack of a mechanism to link each concept with its specific reference image. We address this with anchored prompts, which embed image anchors as unique tokens within text prompts, guiding accurate referencing during generation. Additionally, we introduce concept embeddings to encode the order of reference images. Our approach, Movie Weaver, seamlessly weaves multiple concepts-including face, body, and animal images-into one video, allowing flexible combinations in a single model. The evaluation shows that Movie Weaver outperforms existing methods for multi-concept video personalization in identity preservation and overall quality.

Recent research has explored methods for updating and modifying factual knowledge in large language models, often focusing on specific multi-layer perceptron blocks. This study expands on this work by examining the effectiveness of existing knowledge editing methods across languages and delving into the role of attention mechanisms in this process. Drawing from the insights gained, we propose Mass-Editing Memory with Attention in Transformers (MEMAT), a method that achieves significant improvements in all metrics while requiring minimal parameter modifications. MEMAT delivers a remarkable 10% increase in magnitude metrics, benefits languages not included in the training data and also demonstrates a high degree of portability. Our code and data are at https://github.com/dtamayo-nlp/MEMAT.

Large Language Models (LLMs), such as ChatGPT, allow researchers to analyze extensive text corpora with efficiency (Bail, 2024). However, human and algorithmic biases can influence their outputs, reflecting ideological or cultural skewness in the training data (Bender et al., 2021; Kroon et al., 2023). With the growing adoption of LLM-Assisted Content Analysis (LACA)--a method replacing manual coding with LLM-generated datasets (Chew et al., 2023)--the scientific community must ensure these tools enhance understanding rather than reinforce existing biases (Messeri and Crockett, 2024). Human bias is a long-standing issue in content analysis, which traditionally depends on a consensus-oriented research practice. Intercoder agreement is used to ensure reliability, aiming for shared interpretations of text that can be statistically validated (Krippendorff, 1999). However, this emphasis on consensus may inadvertently neglect diverse perspectives, favoring uniform interpretations that risk simplifying the sociocultural complexities embedded in textual connotations.

Creating data reports is time-consuming, as it requires iterative exploration and understanding of data, followed by summarizing the insights. While large language models (LLMs) are powerful tools for data processing and text generation, they often struggle to produce complete data reports that fully meet user expectations. One significant challenge is effectively communicating the entire analysis logic to LLMs. Moreover, determining a comprehensive analysis logic can be mentally taxing for users. To address these challenges, we propose ReSpark, an LLM-based method that leverages existing data reports as references for creating new ones. Given a data table, ReSpark searches for similar-topic reports, parses them into interdependent segments corresponding to analytical objectives, and executes them with new data. It identifies inconsistencies and customizes the objectives, data transformations, and textual descriptions. ReSpark allows users to review real-time outputs, insert new objectives, and modify report content. Its effectiveness was evaluated through comparative and user studies.

Signed networks, where edges are labeled as positive or negative to indicate friendly or antagonistic interactions, offer a natural framework for studying polarization, trust, and conflict in social systems. Detecting meaningful group structures in these networks is crucial for understanding online discourse, political division, and trust dynamics. A key challenge is to identify groups that are cohesive internally yet antagonistic externally, while allowing for neutral or unaligned vertices. In this paper, we address this problem by identifying $k$ polarized communities that are large, dense, and balanced in size. We develop an approach based on Frank-Wolfe optimization, leading to a local search procedure with provable convergence guarantees. Our method is both scalable and efficient, outperforming state-of-the-art baselines in solution quality while remaining competitive in terms of computational efficiency.

This paper addresses the challenge of automatically extracting attributes from news article web pages across multiple languages. Recent neural network models have shown high efficacy in extracting information from semi-structured web pages. However, these models are predominantly applied to domains like e-commerce and are pre-trained using English data, complicating their application to web pages in other languages. We prepared a multilingual dataset comprising 3,172 marked-up news web pages across six languages (English, German, Russian, Chinese, Korean, and Arabic) from 161 websites. The dataset is publicly available on GitHub. We fine-tuned the pre-trained state-of-the-art model, MarkupLM, to extract news attributes from these pages and evaluated the impact of translating pages into English on extraction quality. Additionally, we pre-trained another state-of-the-art model, DOM-LM, on multilingual data and fine-tuned it on our dataset. We compared both fine-tuned models to existing open-source news data extraction tools, achieving superior extraction metrics.

An external streaming device is the best way to access online video services without replacing your entire TV. By plugging one of these devices into your TV's HDMI port, you'll be able to use apps like Netflix and Hulu, possibly with a faster and smoother experience than your TV's built-in software. But between Roku, Fire TV, Google TV, and Apple TV, picking a streaming device can be overwhelming. We've reviewed them all and have come up with a list of recommendations for every need and budget. As TechHive's resident cord-cutting expert, I've reviewed practically every streaming device that's come out over the past decade, and I've been a cord-cutter myself since 2008.

I've recommended the DJI Mini 4K to just about everyone I know if they're trying to get into aerial photography and videography. It's the perfect balance of advanced features, simplicity, and cost for the average person. But right now, you can grab it with a controller for just 239 at Amazon. This deal sold out on Black Friday, so don't dilly-dally if you want to get up in the air. This craft weighs 249 grams, which seems like an odd weight, but it's actually very strategic.