Breakthroughs, discoveries, and DIY tips sent every weekday. Don't let the appetizing description fool you. When planetary scientists say the interior of Mars resembles a rocky road brownie more than a piece of buttery shortbread, the tasty metaphor masks billions of years of geological violence. In a re-examination of previous observations collected by NASA's decommissioned InSight probe, researchers have discovered that the Martian mantle is embedded with ancient fragments measuring as much as 2.5 miles wide. The data is detailed in a study published on August 28 in Nature.

--T extbook question answering (TQA) is a complex task, requiring the interpretation of complex multimodal context. Although recent advances have improved overall performance, they often encounter difficulties in educational settings where accurate semantic alignment and task-specific document retrieval are essential. In this paper, we propose a novel approach to multimodal textbook question answering by introducing a mechanism for enhancing semantic representations through multi-objective joint training. Our model, Joint Embedding Training With Ranking Supervision for T extbook Question Answering (JETRTQA), is a multimodal learning framework built on a retriever-generator architecture that uses a retrieval-augmented generation setup, in which a multimodal large language model generates answers. JETRTQA is designed to improve the relevance of retrieved documents in complex educational contexts. Unlike traditional direct scoring approaches, JETRTQA learns to refine the semantic representations of questions and documents through a supervised signal that combines pairwise ranking and implicit supervision derived from answers. We evaluate our method on the CK12-QA dataset and demonstrate that it significantly improves the discrimination between informative and irrelevant documents, even when they are long, complex, and multimodal. JETRTQA outperforms the previous state of the art, achieving a 2.4% gain in accuracy on the validation set and 11.1% on the test set. EXTBOOK question answering (TQA) has emerged as a central challenge in natural language processing because the complexity of educational content requires deep semantic reasoning. TQA involves the analysis of structured, often lengthy, educational documents that are frequently multimodal, incorporating elements such as diagrams, tables, or explanatory images. The retrieved information is then used to generate answers. This process is not a simple fusion; it demands a strategic approach to overcome the fundamental limitations of traditional question-answering (QA) models, which are often unable to effectively handle long, complex, or out-of-domain contexts [1], [2].

Understanding the internal reasoning behind the predictions of machine learning systems is increasingly vital, given their rising adoption and acceptance. While previous approaches, such as LIME, generate algorithmic explanations by attributing importance to input features for individual examples, recent research indicates that practitioners prefer examining language explanations that explain sub-groups of examples. In this paper, we introduce MaNtLE, a model-agnostic natural language explainer that analyzes multiple classifier predictions and generates faithful natural language explanations of classifier rationale for structured classification tasks. MaNtLE uses multi-task training on thousands of synthetic classification tasks to generate faithful explanations. Simulated user studies indicate that, on average, MaNtLE-generated explanations are at least 11% more faithful compared to LIME and Anchors explanations across three tasks. Human evaluations demonstrate that users can better predict model behavior using explanations from MaNtLE compared to other techniques

Scientists have discovered a hidden layer of Earth, which sits 100 miles below the surface and covers at least 44 percent of the planet. This previously unknown region of molten rock is part of the asthenosphere, located under tectonic plates in the upper mantle, which forms a soft boundary that allows the solid rock slabs to move. While the discovery is significant, it shatters long-held theories that molten rocks influence the asthenosphere's viscosity. Junlin Hua, with the University of Texas, Austin, said in a statement: 'When we think about something melting, we intuitively think that the melt must play a big role in the material's viscosity. 'But what we found is that even where the melt fraction is quite high, its effect on mantle flow is very minor.'

A rare giant squid was discovered dead on a beach in Cape Town, South Africa, months after another washed up six miles away. Twitter user Tim Dee, who found the strange-looking sea creature on Scarborough Beach on Tuesday, shared photos and videos online that show the colorful squid's gigantic eye. 'Giant squid species wrecked on Scarborough beach this morning,' he wrote. Twitter user Tim Dee, who found the strange-looking sea creature (above) on Scarborough Beach on Tuesday, shared photos and videos online that show the colorful squid's gigantic eye Dee's video shows a marine biologist pulling back flesh to reveal the squid's huge beak that it uses for hunting and fishing. The sea creature, which looks like something Salvador Dali would have painted, is also known for having a very large eye - usually up to 11 inches in diameter with a 3.5 inch pupil.

Machine learning methods were applied to reconsider the results of several passive seismic experiments in Finland. We created datasets from different stages of the receiver function technique and processed them with one of basic machine learning algorithms. All the results were obtained uniformly with the $k$-nearest neighbors algorithm. The first result is the Moho depth map of the region. Another result is the delineation of the near-surface low $S$-wave velocity layer. There are three such areas in the Northern, Southern, and central parts of the region. The low $S$-wave velocity in the Northern and Southern areas can be linked to the geological structure. However, we attribute the central low $S$-wave velocity area to a large number of water-saturated cracks in the upper 1-5 km. Analysis of the structure of this area leads us to the conclusion that macrofracturing was caused by the last deglaciation.

The characterization of an exoplanet's interior is an inverse problem, which requires statistical methods such as Bayesian inference in order to be solved. Current methods employ Markov Chain Monte Carlo (MCMC) sampling to infer the posterior probability of planetary structure parameters for a given exoplanet. These methods are time consuming since they require the calculation of a large number of planetary structure models. To speed up the inference process when characterizing an exoplanet, we propose to use conditional invertible neural networks (cINNs) to calculate the posterior probability of the internal structure parameters. cINNs are a special type of neural network which excel in solving inverse problems. We constructed a cINN using FrEIA, which was then trained on a database of $5.6\cdot 10^6$ internal structure models to recover the inverse mapping between internal structure parameters and observable features (i.e., planetary mass, planetary radius and composition of the host star). The cINN method was compared to a Metropolis-Hastings MCMC. For that we repeated the characterization of the exoplanet K2-111 b, using both the MCMC method and the trained cINN. We show that the inferred posterior probability of the internal structure parameters from both methods are very similar, with the biggest differences seen in the exoplanet's water content. Thus cINNs are a possible alternative to the standard time-consuming sampling methods. Indeed, using cINNs allows for orders of magnitude faster inference of an exoplanet's composition than what is possible using an MCMC method, however, it still requires the computation of a large database of internal structures to train the cINN. Since this database is only computed once, we found that using a cINN is more efficient than an MCMC, when more than 10 exoplanets are characterized using the same cINN.

NASA's InSight lander has measured one of the biggest and longest marsquakes yet, which featured tremors of 4.2 magnitude lasting nearly an hour and a half, the space agency said. The robotic seismometre celebrated 1,000 days on the Red Planet on September 18, when it detected the largest tremor since it arrived at the Elysium Planitia in 2018. The 4.2 magnitude quake equals the largest detected so far on Mars, but on Earth that would be considered'light', with more than 10,000 earthquakes of that level detected every year, feeling like a light rumble that would make dishes shake. The lander was only able to make the measurement after efforts to clear dust from its solar panels earlier in the year - keeping the seismometre operating. The team took a counterintuitive approach to achieving this by sprinkling one solar panel with larger sand grains in the hope wind would blow it across the other panel and result in clearing enough of the dust to allow power to enter the device.

The Earth's core is growing lopsided, scientists have discovered, but it is unclear why. The solid-iron core in the middle of the planet has been growing faster under Indonesia's Banda Sea, seismologists at the University of California in Berkeley found. The growth on one side of the molten metal is the product of iron crystals that form as the molten iron cools, but something in the Earth's outer core or mantle under the south Asian country is removing heat at a faster rate than on the opposite side, under Brazil. The faster the cooling, the faster that iron crystallisation occurs – and the faster the growth increases. Such a disparity has significant implications for the Earth's magnetic field, and the convection currents in the core that generate the field are what protects us from dangerous solar particles.

Seismic wave data has revealed giant structures 2900 kilometres beneath the surface of Earth, at the boundary between Earth's molten core and solid mantle. The structure, known as an ultra-low velocity (ULV) zone, is about 1000 kilometres in diameter and 25 kilometres thick, says Kim. These structures are called ULV zones because seismic waves pass through them at slower velocities, but what they are made of is still a mystery. They might be chemically distinct from Earth's iron–nickel alloy core and silicate rock mantle, or have different thermal properties. The researchers discovered the structure while analysing 7000 records of seismic activity from earthquakes that occurred around the Pacific Ocean basin between 1990 and 2018.