Deep learning has recently gained immense popularity in the Earth sciences as it enables us to formulate purely data-driven models of complex Earth system processes. Deep learning-based weather prediction (DLWP) models have made significant progress in the last few years, achieving forecast skills comparable to established numerical weather prediction (NWP) models with comparatively lesser computational costs. In order to train accurate, reliable, and tractable DLWP models with several millions of parameters, the model design needs to incorporate suitable inductive biases that encode structural assumptions about the data and modelled processes. When chosen appropriately, these biases enable faster learning and better generalisation to unseen data. Although inductive biases play a crucial role in successful DLWP models, they are often not stated explicitly and how they contribute to model performance remains unclear. Here, we review and analyse the inductive biases of six state-of-the-art DLWP models, involving a deeper look at five key design elements: input data, forecasting objective, loss components, layered design of the deep learning architectures, and optimisation methods. We show how the design choices made in each of the five design elements relate to structural assumptions. Given recent developments in the broader DL community, we anticipate that the future of DLWP will likely see a wider use of foundation models -- large models pre-trained on big databases with self-supervised learning -- combined with explicit physics-informed inductive biases that allow the models to provide competitive forecasts even at the more challenging subseasonal-to-seasonal scales.

Binary code similarity detection (BCSD) is widely used in various binary analysis tasks such as vulnerability search, malware detection, clone detection, and patch analysis. Recent studies have shown that the learning-based binary code embedding models perform better than the traditional feature-based approaches. In this paper, we propose a novel transformer-based binary code embedding model named UniASM to learn representations of the binary functions. We design two new training tasks to make the spatial distribution of the generated vectors more uniform, which can be used directly in BCSD without any fine-tuning. In addition, we present a new tokenization approach for binary functions, which increases the token's semantic information and mitigates the out-of-vocabulary (OOV) problem. We conduct an in-depth analysis of the factors affecting model performance through ablation experiments and obtain some new and valuable findings. The experimental results show that UniASM outperforms the state-of-the-art (SOTA) approach on the evaluation dataset. The average scores of Recall@1 on cross-compilers, cross-optimization levels, and cross-obfuscations are 0.77, 0.72, and 0.72. Besides, in the real-world task of known vulnerability search, UniASM outperforms all the current baselines.

Deep Neural Networks (DNNs) have become ubiquitous due to their performance on prediction and classification problems. However, they face a variety of threats as their usage spreads. Model extraction attacks, which steal DNNs, endanger intellectual property, data privacy, and security. Previous research has shown that system-level side-channels can be used to leak the architecture of a victim DNN, exacerbating these risks. We propose two DNN architecture extraction techniques catering to various threat models. The first technique uses a malicious, dynamically linked version of PyTorch to expose a victim DNN architecture through the PyTorch profiler. The second, called EZClone, exploits aggregate (rather than time-series) GPU profiles as a side-channel to predict DNN architecture, employing a simple approach and assuming little adversary capability as compared to previous work. We investigate the effectiveness of EZClone when minimizing the complexity of the attack, when applied to pruned models, and when applied across GPUs. We find that EZClone correctly predicts DNN architectures for the entire set of PyTorch vision architectures with 100% accuracy. No other work has shown this degree of architecture prediction accuracy with the same adversarial constraints or using aggregate side-channel information. Prior work has shown that, once a DNN has been successfully cloned, further attacks such as model evasion or model inversion can be accelerated significantly.

The Gaussian process state-space model (GPSSM) has garnered considerable attention over the past decade. However, the standard GP with a preliminary kernel, such as the squared exponential kernel or Mat\'{e}rn kernel, that is commonly used in GPSSM studies, limits the model's representation power and substantially restricts its applicability to complex scenarios. To address this issue, we propose a new class of probabilistic state-space models called TGPSSMs, which leverage a parametric normalizing flow to enrich the GP priors in the standard GPSSM, enabling greater flexibility and expressivity. Additionally, we present a scalable variational inference algorithm that offers a flexible and optimal structure for the variational distribution of latent states. The proposed algorithm is interpretable and computationally efficient due to the sparse GP representation and the bijective nature of normalizing flow. Moreover, we incorporate a constrained optimization framework into the algorithm to enhance the state-space representation capabilities and optimize the hyperparameters, leading to superior learning and inference performance. Experimental results on synthetic and real datasets corroborate that the proposed TGPSSM outperforms several state-of-the-art methods. The accompanying source code is available at \url{https://github.com/zhidilin/TGPSSM}.

Poverty maps are essential tools for governments and NGOs to track socioeconomic changes and adequately allocate infrastructure and services in places in need. Sensor and online crowd-sourced data combined with machine learning methods have provided a recent breakthrough in poverty map inference. However, these methods do not capture local wealth fluctuations, and are not optimized to produce accountable results that guarantee accurate predictions to all sub-populations. Here, we propose a pipeline of machine learning models to infer the mean and standard deviation of wealth across multiple geographically clustered populated places, and illustrate their performance in Sierra Leone and Uganda. These models leverage seven independent and freely available feature sources based on satellite images, and metadata collected via online crowd-sourcing and social media. Our models show that combined metadata features are the best predictors of wealth in rural areas, outperforming image-based models, which are the best for predicting the highest wealth quintiles. Our results recover the local mean and variation of wealth, and correctly capture the positive yet non-monotonous correlation between them. We further demonstrate the capabilities and limitations of model transfer across countries and the effects of data recency and other biases. Our methodology provides open tools to build towards more transparent and interpretable models to help governments and NGOs to make informed decisions based on data availability, urbanization level, and poverty thresholds.

Millions have dreamt about driving across the US -- but now science has taken the guesswork out of planning the epic trip. An Orgeon-based data scientist used a sophisticated algorithm to generate the perfect journey, factoring in things like logistics for traffic and the most scenic routes for a three-month vacation. Each stop is a national natural landmark, national historic site, national park or national monument, all in the lower 48 states. In total the route boasts 50 iconic sights. The 13,699-mile-long route stops at Mount Rushmore, the San Francisco Cable Cars, Cape Canaveral Air Force Station and other well-known spots.

Fun and easy #copywriting hack from YAHOO. One of my buddies, Justin Brooke, was telling me and a bunch of other people about two websites, health.yahoo.com And these are ones that I've seen but I kind of forgot about them. And what Yahoo does is they split up their stuff like USA Today used to do, and they split it up into these different category pages. But the cool thing about it is you can use AI, give AI these angles and say, hey, use these ads that you see as inspiration to make some new headlines or new angles. But what you want to do is look at health.yahoo.com

As AI advances, could it replace your travel agent? To investigate just how effective a holiday planner AI can be, MailOnline Travel asked two chatbots - ChatGPT, created by California AI firm OpenAI, and Google's Bard - to plan a variety of trips. Scroll down to see the answers the chatbots provided, from hotel recommendations in Iraq to advice on planning budget sun holidays, honeymoons and stag weekends away. For a budget break in the sun, Bard recommended jetting off to Bulgaria, where it says that you can find a week-long all-inclusive holiday'for as little as £200'. MailOnline Travel asked ChatGPT and Google's Bard to plan a variety of holidays.

Microsoft has announced a new, slightly more sustainable Xbox controller. Arriving as an Earth Day promotion, the Xbox Remix Special Edition wireless controller uses recycled materials from old gamepads, auto headlight covers and reclaimed CDs (among other sources) to give each accessory a unique look – but no special functionality. Microsoft describes the combination of recycled resins with regrind as creating "custom, earth-tone colors with subtle variations, swirling, markings, and texturing – giving each Remix Special Edition controller its own look and feel." While it's hard to see that on the press images, it should result in a satisfying textured pattern on the bumpers and side grip. The company also bundles an Xbox Rechargeable Battery Pack with each gamepad, ensuring fewer AA batteries head to landfills.

We introduce the Segment Anything (SA) project: a new task, model, and dataset for image segmentation. Using our efficient model in a data collection loop, we built the largest segmentation dataset to date (by far), with over 1 billion masks on 11M licensed and privacy respecting images. The model is designed and trained to be promptable, so it can transfer zero-shot to new image distributions and tasks. We evaluate its capabilities on numerous tasks and find that its zero-shot performance is impressive -- often competitive with or even superior to prior fully supervised results. We are releasing the Segment Anything Model (SAM) and corresponding dataset (SA-1B) of 1B masks and 11M images at https://segment-anything.com to foster research into foundation models for computer vision.