We apply one-dimensional convolutional neural networks to the Frobenius traces of elliptic curves over $\mathbb{Q}$ and evaluate and interpret their predictive capacity. In keeping with similar experiments by Kazalicki--Vlah, Bujanović--Kazalicki--Novak, and Pozdnyakov, we observe high accuracy predictions for the analytic rank across a range of conductors. We interpret the prediction using saliency curves and explore the interesting interplay between murmurations and Mestre--Nagao sums, the details of which vary with the conductor and the (predicted) rank.

Under Armour data breach investigation reveals 72 million customer email addresses were exposed after researchers found personal information on hacker forums.

A.I. was going to be what broke our shared reality. It turns out, it wasn't needed. Enter your email to receive alerts for this author. You can manage your newsletter subscriptions at any time. You're already subscribed to the aa_Molly_Olmstead newsletter.

Amazon.com is using an experimental artificial intelligence tool to duplicate independent sellers' product listings, sometimes without their knowledge, then make purchases on behalf of Amazon customers. Sometime around Christmas, Sarah Burzio noticed that the holiday sales bump for her stationery business included some mysterious new customers: a flurry of orders from anonymous email addresses associated with Amazon.com. Burzio, who doesn't sell her products on the retail giant's site, soon discovered that Amazon had duplicated her product listings and made purchases on behalf of Amazon customers under email addresses that read like gibberish followed by buyforme.amazon. I didn't worry about, it to be honest," she said. In a time of both misinformation and too much information, quality journalism is more crucial than ever. By subscribing, you can help us get the story right.

Google rolls out Gmail address replacement tool that converts old email into automatic alias, allowing users to receive messages at both addresses.

How can one publish a dataset with sensitive attributes in a way that both preserves privacy and enables joins with other datasets on those same sensitive attributes? This problem arises in many contexts, e.g., a hospital and an airline may want to jointly determine whether people who take long-haul flights are more likely to catch respiratory infections. If they join their data by a common keyed user identifier such as email address, they can determine the answer, though it breaks privacy. This paper shows how the hospital can generate a private sketch and how the airline can privately join with the hospital's sketch by email address. The proposed solution satisfies pure differential privacy and gives approximate answers to linear queries and optimization problems over those joins. Whereas prior work such as secure function evaluation requires sender/receiver interaction, a distinguishing characteristic of the proposed approach is that it is non-interactive. Consequently, the sketch can be published to a repository for any organization to join with, facilitating data discovery. The accuracy of the method is demonstrated through both theoretical analysis and extensive empirical evidence.

Plus: Cisco discloses a zero-day with no available patch, Venezuela accuses the US of a cyberattack, and more. Federal contracting records reviewed by WIRED this week show that United States Customs and Border Protection is transitioning from testing small drones to using them as standard surveillance tools, a move that will further expand CBP's already extensive dragnet that in some cases extends far beyond US land borders. Meanwhile, US Immigration and Customs Enforcement is planning to incorporate a broad cybersecurity contract that will include expanding employee surveillance and monitoring . The move comes as the US government is escalating leak investigations and condemning internal dissent. The Chinese-language artificial intelligence app Haotian can be used to create "nearly perfect" face swaps during live video chats, and it is a favorite tool of Southeast Asian scammers.

Accurate and interpretable gait analysis plays a crucial role in the early detection of Parkinsons disease (PD),yet most existing approaches remain limited by single-modality inputs, low robustness, and a lack of clinical transparency. This paper presents an explainable multimodal framework that integrates RGB and Depth (RGB-D) data to recognize Parkinsonian gait patterns under realistic conditions. The proposed system employs dual YOLOv11-based encoders for modality-specific feature extraction, followed by a Multi-Scale Local-Global Extraction (MLGE) module and a Cross-Spatial Neck Fusion mechanism to enhance spatial-temporal representation. This design captures both fine-grained limb motion (e.g., reduced arm swing) and overall gait dynamics (e.g., short stride or turning difficulty), even in challenging scenarios such as low lighting or occlusion caused by clothing. To ensure interpretability, a frozen Large Language Model (LLM) is incorporated to translate fused visual embeddings and structured metadata into clinically meaningful textual explanations. Experimental evaluations on multimodal gait datasets demonstrate that the proposed RGB-D fusion framework achieves higher recognition accuracy, improved robustness to environmental variations, and clear visual-linguistic reasoning compared with single-input baselines. By combining multimodal feature learning with language-based interpretability, this study bridges the gap between visual recognition and clinical understanding, offering a novel vision-language paradigm for reliable and explainable Parkinsons disease gait analysis. Code:https://github.com/manaralnaasan/RGB-D_parkinson-LLM

The Industry Trump Gave the Tech Bros Everything. Why Are They Still Crashing Out? This was supposed to be their year--but a historically unpopular president and fears of an A.I. stock market crash loom large over Silicon Valley. Enter your email to receive alerts for this author. You can manage your newsletter subscriptions at any time.

The current literature on memorization in Natural Language Models, especially Large Language Models (LLMs), poses severe security and privacy risks, as models tend to memorize personally identifying information (PIIs) from training data. We introduce Randomized Masked Fine-Tuning (RMFT), a novel privacy-preserving fine-tuning technique that reduces PII memorization while minimizing performance impact. Using the Enron Email Dataset, we demonstrate that RMFT achieves an 80.81% reduction in Total Extraction Rate and 80.17% reduction in Seen Extraction Rate compared to baseline fine-tuning, outperforming deduplication methods while maintaining only a 5.73% increase in perplexity. We present MaxTER, a Pareto-optimal evaluation framework for assessing privacy-utility tradeoffs, and show the performance of RMFT vs Deduplication by Area Under The Response Curve (AURC) metric.