These genetic libraries plan for worse-case scenarios. An employee at the Leibniz Institute of Plant Genetics and Crop Plant Research in Germany shows off a specimen of frozen plant seeds from the institute's genebank. Breakthroughs, discoveries, and DIY tips sent every weekday. Amid the 872-day siege of Leningrad in the early 1940s, nine people died protecting a library. This library was not for books, but for seeds collected from around the globe.

From colonial roots to sledging, Samantha Johnson takes a look at why this rivalry has always been about more than runs and wickets. Jake Paul vs Anthony Joshua: Is this good for Boxing? Why does Israel play in European Football?

The success of deep learning comes from its ability to capture the hierarchical structure of data by learning high-level representations defined in terms of low-level ones. In this paper we explore self-supervised learning of hierarchical representations of speech by applying multiple levels of Contrastive Predictive Coding (CPC). We observe that simply stacking two CPC models does not yield significant improvements over single-level architectures. Inspired by the fact that speech is often described as a sequence of discrete units unevenly distributed in time, we propose a model in which the output of a low-level CPC module is non-uniformly downsampled to directly minimize the loss of a high-level CPC module. The latter is designed to also enforce a prior of separability and discreteness in its representations by enforcing dissimilarity of successive high-level representations through focused negative sampling, and by quantization of the prediction targets. Accounting for the structure of the speech signal improves upon single-level CPC features and enhances the disentanglement of the learned representations, as measured by downstream speech recognition tasks, while resulting in a meaningful segmentation of the signal that closely resembles phone boundaries.

Pretrained language models (LMs) are susceptible to generate text with nonfactual information. In this work, we measure and improve the factual accuracy of large-scale LMs for open-ended text generation. We design the FactualityPrompts test set and metrics to measure the factuality of LM generations. Based on that, we study the factual accuracy of LMs with parameter sizes ranging from 126M to 530B. Interestingly, we find that larger LMs are more factual than smaller ones, although a previous study suggests that larger LMs can be less truthful in terms of misconceptions. In addition, popular sampling algorithms (e.g., top-p) in open-ended text generation can harm the factuality due to the ``uniform randomness'' introduced at every sampling step. We propose the factual-nucleus sampling algorithm that dynamically adapts the randomness to improve the factuality of generation while maintaining quality. Furthermore, we analyze the inefficiencies of the standard training method in learning correct associations between entities from factual text corpus (e.g., Wikipedia). We propose a factuality-enhanced training method that uses TopicPrefix for better awareness of facts and sentence completion as the training objective, which can vastly reduce the factual errors.

We propose a scalable framework for inference in a continuous sigmoidal Cox process that assumes the corresponding intensity function is given by a Gaussian process (GP) prior transformed with a scaled logistic sigmoid function. We present a tractable representation of the likelihood through augmentation with a superposition of Poisson processes. This view enables a structured variational approximation capturing dependencies across variables in the model. Our framework avoids discretization of the domain, does not require accurate numerical integration over the input space and is not limited to GPs with squared exponential kernels. We evaluate our approach on synthetic and real-world data showing that its benefits are particularly pronounced on multivariate input settings where it overcomes the limitations of mean-field methods and sampling schemes. We provide the state of-the-art in terms of speed, accuracy and uncertainty quantification trade-offs.

Amazon Ring introduces AI-powered facial recognition to identify friends and delivery drivers, while privacy advocates warn of surveillance risks despite convenience benefits.

The last few years have witnessed a surge in methods for symbolic regression, from advances in traditional evolutionary approaches to novel deep learning-based systems. Individual works typically focus on advancing the state-of-the-art for one particular class of solution strategies, and there have been few attempts to investigate the benefits of hybridizing or integrating multiple strategies. In this work, we identify five classes of symbolic regression solution strategies---recursive problem simplification, neural-guided search, large-scale pre-training, genetic programming, and linear models---and propose a strategy to hybridize them into a single modular, unified symbolic regression framework. Based on empirical evaluation using SRBench, a new community tool for benchmarking symbolic regression methods, our unified framework achieves state-of-the-art performance in its ability to (1) symbolically recover analytical expressions, (2) fit datasets with high accuracy, and (3) balance accuracy-complexity trade-offs, across 252 ground-truth and black-box benchmark problems, in both noiseless settings and across various noise levels. Finally, we provide practical use case-based guidance for constructing hybrid symbolic regression algorithms, supported by extensive, combinatorial ablation studies.

We study the recent progress on dynamic view synthesis (DVS) from monocular video. Though existing approaches have demonstrated impressive results, we show a discrepancy between the practical capture process and the existing experimental protocols, which effectively leaks in multi-view signals during training. We define effective multi-view factors (EMFs) to quantify the amount of multi-view signal present in the input capture sequence based on the relative camera-scene motion. We introduce two new metrics: co-visibility masked image metrics and correspondence accuracy, which overcome the issue in existing protocols. We also propose a new iPhone dataset that includes more diverse real-life deformation sequences. Using our proposed experimental protocol, we show that the state-of-the-art approaches observe a 1-2 dB drop in masked PSNR in the absence of multi-view cues and 4-5 dB drop when modeling complex motion. Code and data can be found at http://hangg7.com/dycheck.

Why is Israel still in southern Lebanon? A war to shape Lebanon's future Two people have been killed in an Israeli drone strike on a minibus in eastern Lebanon as near-daily ceasefire violations continue, Lebanese state media reported. Lebanon's National News Agency (NNA) said on Thursday that the drone hit the vehicle on the Hosh al-Sayyed Ali road in the Hermel district. Israeli military spokesperson Avichay Adraee claimed on X that Thursday's strike targeted a "terrorist operative" in al-Nasiriyah in eastern Lebanon. The attack came hours after a passerby was injured in an Israeli drone strike targeting a car in the town of Jennata in the Tyre district of southern Lebanon late on Wednesday.

This year, the right-to-repair movement got a boost from--surprisingly--big tech, tariffs, and economic downturn. It has been a big year for the right to repair, the movement of advocates pushing for people to be able to fix their own electronics and equipment without manufacturer approval. The issue has gathered broad support from technologists, farmers, military leaders, and politicians on both sides of the aisle. It is popular with just about everyone--except the companies who stand to gain if the parts, instructions, and tools necessary to fix their products remain under lock and key. Three US states passed right-to-repair laws this year, including in heavily Republican states like Texas where the measure received a unanimous vote in both the House and Senate.