Three meteor showers will peak this month. This delightfully detailed false color image of Saturn is a combination of three images taken in January 1998 by the Hubble Space Telescope and shows the ringed planet in reflected infrared light. Different colors indicated varying heights and compositions of cloud layers generally thought to consist of ammonia ice crystals. The eye-catching rings cast a shadow on Saturn's upper hemisphere, while the bright stripe seen within the left portion of the shadow is infrared sunlight streaming through the large gap in the rings known as the Cassini Division. Breakthroughs, discoveries, and DIY tips sent every weekday.

Trump dollar coin design released by Treasury... and it's inspired by an iconic political photo Top plastic surgeons reveal secrets behind Taylor Swift's'changing' face: 'It is looking very full' Shroud of Turin mystery deepens as surgeon spots hidden detail that points to Jesus' resurrection Hollywood A-listers pay me $50,000 to cure their drug addicted nepo-babies because they can't afford for these secrets to go public I'm no longer sleeping with my husband - and never will again, says MOLLY RYDDELL. I love him, but counted down the moments until he climaxed. Then I couldn't bear it any more and the truth spilled out... so many women feel the same Fans erupt at Taylor Swift's'dig' at Travis Kelce's ex Kayla Nicole in wild The Life of a Showgirl track Lori Loughlin's husband Mossimo Giannulli seen with mystery brunette in tiny skirt day after shock split The truth about Keith Urban's guitarist'other woman' Maggie Baugh revealed amid Nicole Kidman divorce Taylor, your album should be'Life of a Callgirl'. KENNEDY's appalled take on Swift's new record... and its ultra-vivid sex shout outs for Travis the Sasquatch I was so happy after trying a trendy new cosmetic procedure. But 10 years later I suffered a devastating side effect... the doctor had lied The'middle-class kinks' saving marriages: Wives reveal the eight buzzy sex trends that revived their lagging libidos - including the fantasy husbands are secretly obsessed with I'm a woman with autism... here are the signs you might be masking, even from yourself Cake-faced 90s sitcom star looks unrecognizable as she ditches the heavy eyeshadow for an LA errand run can you guess who?

Synthesizability in small molecule generative design remains a bottleneck. Existing works that do consider synthesizability can output predicted synthesis routes for generated molecules. However, there has been minimal attention in addressing the ease of synthesis and enabling flexibility to incorporate desired reaction constraints. In this work, we propose a small molecule generative design framework that enables steerable and granular synthesizability control. Generated molecules satisfy arbitrary multi-parameter optimization objectives with predicted synthesis routes containing pre-defined allowed reactions, while optionally avoiding others. One can also enforce that all reactions belong to a pre-defined set. We show the capability to mix-and-match these reaction constraints across the most common medicinal chemistry transformations. Next, we show how our framework can be used to valorize industrial byproducts towards de novo optimized molecules. Going further, we demonstrate how granular control over synthesizability constraints can loosely mimic virtual screening of ultra-large make-on-demand libraries. Using only a single GPU, we generate and dock 15k molecules to identify promising candidates in Freedom 4.0 constituting 142B make-on-demand molecules (assessing only 0.00001% of the library). Generated molecules satisfying the reaction constraints have > 90% exact match rate. Lastly, we benchmark our framework against recent synthesizability-constrained generative models and demonstrate the highest sample efficiency even when imposing the additional constraint that all molecules must be synthesizable from a single reaction type. The main theme is demonstrating that a pre-trained generalist molecular generative model can be incentivized to generate property-optimized small molecules under challenging synthesizability constraints through reinforcement learning.

Methods designed to remove such knowledge must do so from all prompt directions, in a multi-lingual capacity and without degrading general model performance. To this end, we introduce the targeted angular reversal (TARS) method of knowledge removal from LLMs. The TARS method firstly leverages the LLM in combination with a detailed prompt to aggregate information about a selected concept in the internal representation space of the LLM. It then refines this approximate concept vector to trigger the concept token with high probability, by perturbing the approximate concept vector with noise and transforming it into token scores with the language model head. The feed-forward weight vectors in the LLM which operate directly on the internal representation space, and have the highest cosine similarity with this refined targeting vector, are then replaced by a reversed targeting vector, thus limiting the ability of the concept to propagate through the model. The modularity of the TARS method allows for a sequential removal of concepts from Llama 3.1 8B, such as the famous literary detective Sherlock Holmes, and the planet Saturn. It is demonstrated that the probability of triggering target concepts can be reduced to 0.00 with as few as 1 TARS edit, whilst simultaneously removing the knowledge bi-directionally. Moreover, knowledge is shown to be removed across all languages despite only being targeted in English. Importantly, TARS has minimal impact on the general model capabilities, as after removing 5 diverse concepts in a modular fashion, there is minimal KL divergence in the next token probabilities of the LLM on large corpora of Wikipedia text (median of 0.002).

If you were an obsessive video game fan in the summer of 1994, you'll remember where you were when Edge magazine's August issue dropped. By then, Sony had already announced its intention to develop the PlayStation console – the previous October – but it was the cover feature in the world's most forward-looking game publication that really blew open the possibilities of the machine. As well as listing its specifications in full, Edge secured enthusiastic statements of support from Capcom, Namco and Konami. One breathless developer told the mag: "It's going to revolutionise the way computers are at the moment." Suddenly, the whole structure of the console games business was being threatened. All it needed was a push.

It is one of the greatest injustices of video game history that the Sega Saturn is widely considered a failure. The console, which was launched in Japan on 22 November 1994, almost two weeks ahead of the PlayStation, is continually and pejoratively compared to its rival. We hear about how Sony produced a high-end machine laser targeted at producing fast 3D graphics, while Sega's engineers had to add an extra graphics chip to the Saturn at the last minute. We read that Sony's Ken Kutaragi provided creators with a much more user-friendly development system. We know that Sony undercut the price of Sega's machine, using its might as a consumer electronics giant to take the financial hit. All of that is true, but what aren't always mentioned are the vast success of the Japanese Saturn launch, and the extraordinary legacy that Sega's 32-bit machine left behind.

Generative molecular design for drug discovery has very recently achieved a wave of experimental validation, with language-based backbones being the most common architectures employed. The most important factor for downstream success is whether an in silico oracle is well correlated with the desired end-point. To this end, current methods use cheaper proxy oracles with higher throughput before evaluating the most promising subset with high-fidelity oracles. The ability to directly optimize high-fidelity oracles would greatly enhance generative design and be expected to improve hit rates. However, current models are not efficient enough to consider such a prospect, exemplifying the sample efficiency problem. In this work, we introduce Saturn, which leverages the Augmented Memory algorithm and demonstrates the first application of the Mamba architecture for generative molecular design. We elucidate how experience replay with data augmentation improves sample efficiency and how Mamba synergistically exploits this mechanism. Saturn outperforms 22 models on multi-parameter optimization tasks relevant to drug discovery and may possess sufficient sample efficiency to consider the prospect of directly optimizing high-fidelity oracles.

Voyager 1 is still alive out there, barreling into the cosmos more than 15 billion miles away. However, a computer problem has kept the mission's loyal support team in Southern California from knowing much more about the status of one of NASA's longest-lived spacecraft. The computer glitch cropped up on November 14, and it affected Voyager 1's ability to send back telemetry data, such as measurements from the craft's science instruments or basic engineering information about how the probe was doing. As a result, the team has no insight into key parameters regarding the craft's propulsion, power, or control systems. "It would be the biggest miracle if we get it back. We certainly haven't given up," said Suzanne Dodd, Voyager project manager at NASA's Jet Propulsion Laboratory, in an interview with Ars.

Staple 1990s yoof TV show The Word has just finished with a raucous live performance by some up-and-coming grunge band and now it's time to play video games. In the decade of the original PlayStation and the Sega Saturn, there was no online multiplayer – if you wanted to compete against human beings, you did it in your living room with friends, and anyone else you found in the pub at closing time. It had to be something accessible, something competitive, something that allowed two or even four people to play at once. It needed to have short rounds, because everyone wanted to play. Invariably that would mean one of two options: a footie sim or a fighting game.

Large language models such as GPT-3 & ChatGPT have transformed deep learning (DL), powering applications that have captured the public's imagination. These models are rapidly being adopted across domains for analytics on various modalities, often by finetuning pre-trained base models. Such models need multiple GPUs due to both their size and computational load, driving the development of a bevy of "model parallelism" techniques & tools. Navigating such parallelism choices, however, is a new burden for end users of DL such as data scientists, domain scientists, etc. who may lack the necessary systems knowhow. The need for model selection, which leads to many models to train due to hyper-parameter tuning or layer-wise finetuning, compounds the situation with two more burdens: resource apportioning and scheduling. In this work, we tackle these three burdens for DL users in a unified manner by formalizing them as a joint problem that we call SPASE: Select a Parallelism, Allocate resources, and SchedulE. We propose a new information system architecture to tackle the SPASE problem holistically, representing a key step toward enabling wider adoption of large DL models. We devise an extensible template for existing parallelism schemes and combine it with an automated empirical profiler for runtime estimation. We then formulate SPASE as an MILP. We find that direct use of an MILP-solver is significantly more effective than several baseline heuristics. We optimize the system runtime further with an introspective scheduling approach. We implement all these techniques into a new data system we call Saturn. Experiments with benchmark DL workloads show that Saturn achieves 39-49% lower model selection runtimes than typical current DL practice.