We investigate the use of Long Short-Term Memory (LSTM) and Decomposition-LSTM (DLSTM) networks, combined with an ensemble algorithm, to predict solar flare occurrences using time-series data from the GOES catalog. The dataset spans from 2003 to 2023 and includes 151,071 flare events. Among approximately possible patterns, 7,552 yearly pattern windows are identified, highlighting the challenge of long-term forecasting due to the Sun's complex, self-organized criticality-driven behavior. A sliding window technique is employed to detect temporal quasi-patterns in both irregular and regularized flare time series. Regularization reduces complexity, enhances large flare activity, and captures active days more effectively. To address class imbalance, resampling methods are applied. LSTM and DLSTM models are trained on sequences of peak fluxes and waiting times from irregular time series, while LSTM and DLSTM, integrated with an ensemble approach, are applied to sliding windows of regularized time series with a 3-hour interval. Performance metrics, particularly TSS (0.74), recall (0.95) and the area under the curve (AUC=0.87) in the receiver operating characteristic (ROC), indicate that DLSTM with an ensemble approach on regularized time series outperforms other models, offering more accurate large-flare forecasts with fewer false errors compared to models trained on irregular time series. The superior performance of DLSTM is attributed to its ability to decompose time series into trend and seasonal components, effectively isolating random noise. This study underscores the potential of advanced machine learning techniques for solar flare prediction and highlights the importance of incorporating various solar cycle phases and resampling strategies to enhance forecasting reliability.

AI services like Perplexity or OpenAI's SearchGPT could be search engine options in a future version of Safari, Bloomberg reports. The tentative plans were shared by Eddy Cue, Apple's senior vice president of services, while on the stand for Google's ongoing search antitrust case. Cue was called to testify because of the deal Google and Apple have to keep Google Search as the default search engine on the iPhone. Cue claims Apple has discussed a possible Safari-integration with Perplexity, but didn't share any definitive plans during his testimony. It's clear that he believes AI assistants will inevitably supplant traditional search engines, though.

These settings allow your iPhone to share data that helps third parties target advertisements to you and measure advertisement engagement. Chip Hallett, author of The Ultimate Privacy Playbook, explained how to turn these'dangerous' settings off to ensure that your data is always kept private. To disable them, start by opening the settings app. Then scroll down and tap'Safari.' Then scroll all the way down to the bottom of the screen where it says'Advanced.' Tap this tab, and you should see a toggle on/off button next to'Privacy Preserving Ad Measurement.'

With the rollout of iOS 18 this fall, Apple is introducing some big changes to its iPhones: more customization options for home screens, a redesigned Control Center, support for the RCS text messaging standard, and of course a bunch of generative AI features put under the umbrella of Apple Intelligence. Individual iOS apps are getting upgrades too, including Safari, and one of the new features you'll notice in the web browser once you've got iOS 18 installed is the option to remove "distracting" items from a page. It's called Distraction Control, and the idea is you can cut out pieces of a page you're not necessarily interested in, like images or menus. This isn't the Reader mode that reformats pages so only the main text and images are showing (and which itself is getting an update in iOS 18). It's not an ad blocker either, because you won't be able to persistently hide ads or any other frequently updated content. But it is a potentially useful tool to improve the web browsing experience.

About a month after Apple announced it at WWDC 2024, macOS Sequoia is available to test-drive as a public beta. Although we don't recommend installing it on your primary Mac, here's how to get the 2024 version of macOS up and running ahead of its official rollout in the fall. First, you'll need a recent Mac to run the Sequoia public beta. Apple's software supports the following models: You'll notice that list still includes (up to) the last few generations of Intel Macs, so Apple may still be several years away from requiring Apple Silicon for its latest software. However, Apple Intelligence, which isn't yet included in the beta, will require a Mac with an M-series chip when it's available. Macs don't have automatic iCloud system backups like iOS devices, so you'll want to back up your Mac with Time Machine before installing.

Referring Expression Segmentation (RES) aims to provide a segmentation mask of the target object in an image referred to by the text (i.e., referring expression). Existing methods require large-scale mask annotations. Moreover, such approaches do not generalize well to unseen/zero-shot scenarios. To address the aforementioned issues, we propose a weakly-supervised bootstrapping architecture for RES with several new algorithmic innovations. To the best of our knowledge, ours is the first approach that considers only a fraction of both mask and box annotations (shown in Figure 1 and Table 1) for training. To enable principled training of models in such low-annotation settings, improve image-text region-level alignment, and further enhance spatial localization of the target object in the image, we propose Cross-modal Fusion with Attention Consistency module. For automatic pseudo-labeling of unlabeled samples, we introduce a novel Mask Validity Filtering routine based on a spatially aware zero-shot proposal scoring approach. Extensive experiments show that with just 30% annotations, our model SafaRi achieves 59.31 and 48.26 mIoUs as compared to 58.93 and 48.19 mIoUs obtained by the fully-supervised SOTA method SeqTR respectively on RefCOCO+@testA and RefCOCO+testB datasets. SafaRi also outperforms SeqTR by 11.7% (on RefCOCO+testA) and 19.6% (on RefCOCO+testB) in a fully-supervised setting and demonstrates strong generalization capabilities in unseen/zero-shot tasks.

Apple has officially unveiled the latest version of its operating system for Mac. This time around, Apple stuck to its "California places" naming convention and went with macOS Sequoia. Also known as macOS 15, the new OS packs a ton of new capabilities onto the desktop, including a password management app, video conferencing tools, and updates to Safari, as well as all the features that come with Apple Intelligence--the company's new artificial intelligence–powered system. Below, we break down all these new features that will become available in macOS Sequoia when it ships this fall. Be sure to also check out our iOS 18 and iPadOS 18 feature roundup for all the new features coming to your iPhone and iPad, and our look at what's new in watchOS 11.

Existing works in federated learning (FL) often assume an ideal system with either full client or uniformly distributed client participation. However, in practice, it has been observed that some clients may never participate in FL training (aka incomplete client participation) due to a myriad of system heterogeneity factors. A popular approach to mitigate impacts of incomplete client participation is the server-assisted federated learning (SA-FL) framework, where the server is equipped with an auxiliary dataset. However, despite SA-FL has been empirically shown to be effective in addressing the incomplete client participation problem, there remains a lack of theoretical understanding for SA-FL. Meanwhile, the ramifications of incomplete client participation in conventional FL are also poorly understood. These theoretical gaps motivate us to rigorously investigate SA-FL. Toward this end, we first show that conventional FL is {\em not} PAC-learnable under incomplete client participation in the worst case. Then, we show that the PAC-learnability of FL with incomplete client participation can indeed be revived by SA-FL, which theoretically justifies the use of SA-FL for the first time. Lastly, to provide practical guidance for SA-FL training under {\em incomplete client participation}, we propose the $\mathsf{SAFARI}$ (server-assisted federated averaging) algorithm that enjoys the same linear convergence speedup guarantees as classic FL with ideal client participation assumptions, offering the first SA-FL algorithm with convergence guarantee. Extensive experiments on different datasets show $\mathsf{SAFARI}$ significantly improves the performance under incomplete client participation.

Diffusion models have recently emerged as a promising framework for Image Restoration (IR), owing to their ability to produce high-quality reconstructions and their compatibility with established methods. Existing methods for solving noisy inverse problems in IR, considers the pixel-wise data-fidelity. In this paper, we propose SaFaRI, a spatial-and-frequency-aware diffusion model for IR with Gaussian noise. Our model encourages images to preserve data-fidelity in both the spatial and frequency domains, resulting in enhanced reconstruction quality. We comprehensively evaluate the performance of our model on a variety of noisy inverse problems, including inpainting, denoising, and super-resolution. Our thorough evaluation demonstrates that SaFaRI achieves state-of-the-art performance on both the ImageNet datasets and FFHQ datasets, outperforming existing zero-shot IR methods in terms of LPIPS and FID metrics.

Arc, a browser initially built just for the Mac, has been expanding lately. The Browser Company announced a beta of its Windows version last month, and today they're bringing the Arc experience to the iPhone with Arc Search. As the name implies, the new app is focused on searching -- when you open the app, you're met with a keyboard and search field, not your usual collection of tabs. And rather than just serving up simple search results from Google or your engine of choice, Arc scans the internet for various sources and creates a "page for me" that pulls together a bunch of info on your desired query. For example, I just searched for "What happened in the Detroit Lions game?" and was met with details about a controversial two-point conversion that was overturned and how it ultimately affected the game's outcome, which was a three-point Lions loss.