Nature, Published online: 19 December 2024; doi:10.1038/s41586-024-08526-2A reinforced lunar dynamo recorded by Chang'e-6 farside basalt

A Hideout in Path of Exile 2 is a personal, customizable hub area where all crafting facilities, merchant NPCs, and the end-game mapping function can be accessed. Players who haven't yet unlocked their hideout will want to know how to go about obtaining one of their own.

Roguelike games are a blast to play through, turning the simple act of repetition into a core part of the gameplay loop as players try their hardest to overcome certain hurdles in their quest to become better at the game in question.

Strands is a daily word-search puzzle game that challenges you to come up with a theme and a handful of themed words from just one little clue. In order to win, you will need to be familiar with the category without knowing what it is ahead of time, and you might get caught trying to figure it out.

Togetic, the happiness Pokmon from Johto, can be found in the wild in Pokmon Go. Yes, Togetic can be shiny in Pokmon Go!Togekiss is a great fairy-type attacker, as it has bulk and a decent moveset. It also does see use in PvP, meaning grabbing a ton of Togetic to check their IVs is a worthwhile endeavor.What is the shiny rate for Togetic in Pokmon Go?As per old research by the now-defunct website The Silph Road (via Wayback Machine), the shiny rate for Pokmon on a regular day is approximately one in 500. Togetic is not a confirmed Pokmon that gets a permaboost (meaning that its a rare spawn and thus gets a boosted shiny rate).What can I do to attract more shiny Pokmon?Not much, unfortunately. It appears to be random chance. Shiny Pokmon catch rates are set by developer Niantic, and they are typically only boosted during special events like Community Days or Safari Zones, or in Legendary Raids. There are no consumable items that boost shiny Pokmon rates.Where can I find a list of available shiny Pokmon?LeekDuck keeps a list of currently available shiny Pokmon. Its a helpful visual guide that illustrates what all of the existing shiny Pokmon look like.For more tips, check out Polygons Pokmon Go guides.

beloFX just unveiled its 2024 VFX showreel, showcasing the incredible artistry and technical innovation over the past year in the studio. From blockbusters to groundbreaking series, this reel showcases an array of visual effects, from intricate environment builds to seamless digital doubles and jaw-dropping simulations.With a commitment to pushing creative boundaries, beloFXs global team has delivered high-quality work for some of the most anticipated projects of the year. Their expertise in blending artistry with cutting-edge technology ensures each frame leaves a lasting impact.This reel does not only recognize the excellent work of beloFXs artistic and technological talent but also marks another great year of visual excellence and storytelling.The post beloFX VFX Showreel 2024 appeared first on Vfxexpress.

The Russian Plates team visited Saint Petersburg during pre-production for the series Crime and Punishment to shoot 360-degree footage of some of the citys iconic landscapes. They shot scenic backgrounds of the Neva and Moika rivers, as well as historic streets, for an authentic visual foundation.This material was adapted later for shooting on LED screens, allowing the recording of boat rides and car journey scenes within a controlled studio environment. The easy blend of real-world visuals with virtual production technology allowed the crew to authentically recreate the atmosphere of Saint Petersburg without the challenges that come with on-location shooting.The collaboration between Plus Studio and Zoom Production, directed by Vladimir Mirzoev and cinematography by Matvey Stavitsky, demonstrates a mastery of storytelling and innovative filmmaking. The behind-the-scenes footage is exciting to watch as it reveals how such immersive scenes were created. xovirtualproductionThe post Crime and Punishment Virtual Production on xovirtualproduction appeared first on Vfxexpress.

Bread becomes a "precious and beautiful element" in thisjewellery range created by designer Cindy Xinyi Wu as an ode to the act of baking and the energetic nature of dough.The Rising collection features sculptural rings, brooches, earrings, bangles and necklaces, all with baked bread dough bulging out of intricately shaped metal frameworks.The shapes were produced by allowing the dough to expand freely within the metal bands while rising and baking.The Rising jewellery collection is made with bread doughAfter baking, the bread was dehydrated to harden its form and preserve its appearance.Wu designed the bands themselves to reference the actions of kneading, stretching and folding during the breadmaking process.She channelled years of baking experience into the Rising collection, which she created as her final-year project in the jewellery design undergraduate course at London's Central Saint Martins.The jewellery was made by allowing bread to rise freely and bake around metal frameworksAfter experimenting with baking as a child, Wu said she began to formally study the craft in 2018 and enjoyed not just the joy of eating and sharing bread but the mental retreat provided by the process of breadmaking."Baking requires my complete concentration at every step and careful observation throughout each phase," Wu told Dezeen."Immersing myself fully in the process of making bread helps me relax you could say this hobby has saved me over the years."The metal elements reference how dough looks as it is being stretched and folded"While I might be realistic, orderly, repressed, anxious and a perfectionist in my primary world, whenever I feel crises and a loss of control, baking becomes my retreat," she continued."It transports me to this second world of bread, where I can breathe again and regain my composure," Wu added."Thus, baking is not just a hobby for me; it's a vital escape from the harshness of reality and a way to alleviate stress and anxiety."The baked bread was dehydrated to preserve itHer goal with the collection was both to document her own connection with bread and to elevate the foodstuff to the position of a decorative material, on a par with gemstones."I've always perceived bread as a precious and beautiful element," Wu said."In my work, I intended for bread to take the central role traditionally occupied by gemstones in jewellery, hence my method of integrating bread with metal is influenced by the classic technique of setting gemstones in metal frameworks."Designer Cindy Xinyi Wu sees the bread as serving in the place of gemstonesThe metal frames in Wu's work take many different forms. Some are deeply textured like slabs of pulled dough, some are complex and twisted like the folds of a bun, while others provide a smooth and gleaming counterpoint.The designer said her baking studies had introduced her to the many different metal tools and moulds used to shape bread around the world, which ultimately inspired her to experiment with metal frameworks.Read: Chess set hidden in scarf, watch and rings by Louis Le Joly Senoville"Unlike traditional recipes, where moulds strictly define the bread's shape, I wanted the dough to expand freely and randomly, showcasing its own vitality and the unique aesthetic brought about by its rising," said Wu.Wu hopes that her deep affinity for the material comes across in the collection and inspires viewers to look at bread anew.Wu is a longtime baker who brought her experience to the projectShe points to characteristics like bread's unpredictability, the surprising changes brought on by factors such as temperature or length of fermentation, and the way dough "relaxes" and slumps after being shaped that give this food a unique and loveable character."The aspect of baking I love the most is the dough's expansion during fermentation, which I refer to as 'rising' also the name of my collection," said Wu."In the Rising series, I aim to capture the vitality and inherent energy of dough as it expands, documenting the unique shapes created by its free and random growth."Wu hopes the collection entices viewers to consider the unique character of bread"Watching the dough rise is fascinating; the yeast slowly fills it with air, and bubbles gradually emerge on the surface," she continued."To me, this process feels like breathing, like a pulse, with each batch of dough possessing a distinct personality. This part of the process always feels healing and recharging to me."Another product released for bread lovers this year is the baguette stamp designed by Stphane Humbert-Basset for the French post office. As well as featuring a little baguette illustration, the scratch-and-sniff stamp gives off the aroma of baked goods.The post Bread takes place of gemstones in Rising jewellery collection appeared first on Dezeen.

A screen for anyone who likes to work in the dark, and still be able to see afterwards.

Aligning large language models (LLMs) with human preferences is an essential task in artificial intelligence research. However, current reinforcement learning (RL) methods face notable challenges. Proximal Policy Optimization (PPO) and similar techniques often demand extensive online sampling, which can lead to high computational costs and instability. Offline RL methods like Direct Preference Optimization (DPO) avoid these issues but face difficulties with tasks requiring multi-step reasoning, such as solving mathematical problems or generating complex code. These methods frequently treat the generation process as a single-step problem, neglecting the long-horizon dependencies intrinsic to many reasoning tasks. Additionally, sparse reward functions, which provide feedback only at the conclusion of a reasoning sequence, make intermediate step guidance challenging.Researchers from ByteDance and UCLA have introduced Direct Q-function Optimization (DQO) to address these challenges. DQO frames the response generation process as a Markov Decision Process (MDP) and utilizes the Soft Actor-Critic (SAC) framework. By parameterizing the Q-function directly through the language model, DQO shifts the LLM alignment problem into a structured, step-by-step learning process. Unlike bandit-based methods, DQO incorporates process rewardsintermediate feedback signalsto support multi-step reasoning more effectively.A key feature of DQO is its ability to identify and optimize correct reasoning steps even within partially correct responses. For example, in mathematical problem-solving, DQO assigns higher value to accurate steps and penalizes errors, enabling incremental improvement in reasoning. This makes DQO particularly suitable for tasks requiring detailed, long-horizon decision-making.Technical Implementation and Practical AdvantagesDQOs approach is centered on parameterizing the Q-function using the language model, thereby integrating policy and value functions. The model updates its Q-function and value function based on the Soft Bellman Equation. KL-regularization ensures stable learning and helps prevent overfitting to specific samples.To handle challenges such as high bias in temporal difference errors, DQO employs -return, a mechanism that balances short-term and long-term rewards for more stable training. Importance sampling further enhances DQOs offline learning capabilities by reducing distributional shifts between the training data and the models policy.DQO offers several practical advantages. It eliminates the need for online sampling, reducing computational costs. Moreover, it can learn from unbalanced and negative samples, enhancing its robustness across various scenarios. The use of process rewards helps refine reasoning capabilities while improving alignment with task requirements.Results and InsightsExperimental evaluations of DQO on mathematical reasoning datasetsGSM8K and MATHdemonstrate its effectiveness. On the GSM8K dataset, DQO improved performance from a baseline of 59.06% to 87.26% for greedy generation and from 53.30% to 84.69% for sampling-based generation. These results surpass other baseline methods, including DPO and DRO. Similarly, on the MATH dataset, DQO outperformed baselines, achieving improvements of 1.18% in sampling and 1.40% in greedy generation.Enhancing DQO with process rewards further boosted performance, suggesting its potential to incorporate additional supervisory signals. These results underscore DQOs capability to handle multi-step reasoning tasks effectively and align LLMs with complex objectives.ConclusionDirect Q-function Optimization (DQO) offers a thoughtful approach to reinforcement learning for LLM alignment. By framing response generation as an MDP and utilizing the SAC framework, DQO addresses the limitations of existing methods. Its ability to integrate process rewards, handle unbalanced data, and stabilize training through -return and importance sampling makes it a practical solution for tasks involving multi-step reasoning.Future research could explore applying DQO to other domains, such as code generation and dialogue systems, where long-horizon decision-making is critical. As AI systems evolve to tackle increasingly complex challenges, methods like DQO will play an important role in enhancing the alignment and performance of language models.Check out the Paper. All credit for this research goes to the researchers of this project. Also,dont forget to follow us onTwitter and join ourTelegram Channel andLinkedIn Group. Dont Forget to join our60k+ ML SubReddit. Aswin Ak+ postsAswin AK is a consulting intern at MarkTechPost. He is pursuing his Dual Degree at the Indian Institute of Technology, Kharagpur. He is passionate about data science and machine learning, bringing a strong academic background and hands-on experience in solving real-life cross-domain challenges. [Download] Evaluation of Large Language Model Vulnerabilities Report (Promoted)