This study proposes a data-driven framework for enhancing the accuracy and efficiency of scientific peer review through an open, bottom-up process that estimates reviewer quality. Traditional closed peer review systems, while essential for quality control, are often slow, costly, and subject to biases that can impede scientific progress. Here, we introduce a method that evaluates individual reviewer reliability by quantifying agreement with community consensus scores and applying Bayesian weighting to refine paper quality assessments. We analyze open peer review data from two major scientific conferences, and demonstrate that reviewer-specific quality scores significantly improve the reliability of paper quality estimation. Perhaps surprisingly, we find that reviewer quality scores are unrelated to authorship quality. Our model incorporates incentive structures to recognize high-quality reviewers and encourage broader coverage of submitted papers, thereby mitigating the common "rich-get-richer" pitfall of social media. These findings suggest that open peer review, with mechanisms for estimating and incentivizing reviewer quality, offers a scalable and equitable alternative for scientific publishing, with potential to enhance the speed, fairness, and transparency of the peer review process.

Among the changes ushered in with iOS 18.1, iPadOS 18.1, and macOS 15.1 Sequoia is a new Type to Siri option. This means you can carry on a conversation with Apple's digital assistant without having to talk out loud, which is helpful when you're in a quiet library, busy subway car, or anywhere else you can't really use voice control. The ability to type to Siri has actually been available on Apple devices for several years now, but previously it was hidden away in the Accessibility settings and not all that easy to find. Now Apple has given it much more prominence in its operating systems, so typing is just as straightforward as talking. Breakthroughs, discoveries, and DIY tips sent every weekday.

Weaknesses: I don't think the proposed algorithm to leverage the variance is fully sound. The user memory set Mu is updated on each iteration by first uniformly sampling additional items and then retaining those with higher scores (with probability proportional to softmax of the score). First, for datasets that have lots of items, uniform sampling is very unlikely to produce hard negatives with high scores so this procedure can be highly inefficient. Second, since new samples are likely to have lower scores, one either has to increase the temperature or leave Mu relatively static between iterations. If Mu is static then training can saturate and the model can overfit to these negative examples.

The initial reviews were mixed for this paper. However, during the discussion, a certain consensus emerged regarding the value of this contribution. In particular, the reviews agree that the proposed method is simple and effective. In the proposed study, the method seems to outperform (by a small margin) others consistently. The main negative is the high computational and memory cost of the approach.

If it feels like every piece of home tech is now "smart," you're not far off. The smart home space has grown exponentially in the past few years to include speakers, cameras, locks, lights and even kitchen appliances. There are also different voice assistants and IoT standards to consider, all of which can make it confusing (to say the least) to build your smart home ecosystem from the ground up. Allow us at Engadget to help with that. We've tested dozens of smart home gadgets over the years and continue to test the latest offerings to see which work well and are worth your money. We recommend, before you even dive in, to resist the urge to outfit your whole home in one go.

The proposed algorithm is limited to matrix factorization model, and can be hardly extended to more state-of-art neural network-based latent factor models proposed in recent years. Because the derivate of model parameters with respect to training labels in Equation (7) needs to be a closed form solution as in matrix factorization. This may restrict a broader impact of the proposed solution. I'm concerned about the authors' claim on the trade-off between personalization and accuracy. As emphasized in the title, the authors consider the performance gain of the proposed algorithm as trading personalization for accuracy, but there is no direct empirical evaluation evidence to support this claim.

The paper received overall very positive scores (after communication through author response). All the reviewers agree that the paper made a very interesting contribution from a novel angle to understand the tradeoff between "over-personalization" and accuracy. The empirical results provide convincing support for the claim. I suggest the authors incorporate the feedback from the reviewers in the revision.

Most of the tools in the Humphrey suite are generative AI models - in this case, technology which takes large amounts of information and summarises it in a more digestible format - to be used by the civil service. Among them is Consult, which summarises people's responses to public calls for information. The government says this is currently done by expensive external consultants who bill the taxpayer "around 100,000 every time." Parlex, which the government says helps policymakers search through previous parliamentary debates on a certain topic, is described by The Times as "designed to avoid catastrophic political rows by predicting how MPs will respond". Other changes announced include more efficient data sharing between departments.

Online dating is one of the surest and quickest ways to meet someone. The possibilities of finding what you're looking for are as wide as the internet search. Sometimes you find who you want, but for more and more women, swiping left has exposed them to untold dangers. Increasingly, online dating has become a space where women are being exposed to sexual violence and abuse. This week on Now You Know we talk to Jackie Cruz, a sexual violence researcher, about how women can stay safe on dating apps.

The authors motivate the proposed model with the setting in which items have "true" but unobserved labels/ratings and the observed labels/ratings given by evaluators are potentially incorrect. This differs from the very common problem in recommendation systems or collaborative filtering where evaluators provide their subjective ratings but there is not assumed to be any "true" rating (e.g., users of Netflix giving 1-5 star ratings to movies). This seems like a common but underexplored setting that is worthy of further study within machine learning. The authors are also right to highlight interpretability as a desired aspect of any machine learning solution that may yield post-hoc insights into common human biases and thus suggest corrective measures. This paper does a good job of motivating the proposed model and situating it within the crowdsourcing and human annotation literature.