Abstract--Learning-based image reconstruction models, such as those based on the U-Net, require a large set of labeled images if good generalization is to be guaranteed. In some imaging domains, however, labeled data with pixel-or voxel-level label accuracy are scarce due to the cost of acquiring them. This problem is exacerbated further in domains like medical imaging, where there is no single ground truth label, resulting in large amounts of repeat variability in the labels. Therefore, training reconstruction networks to generalize better by learning from both labeled and unlabeled examples (called semi-supervised learning) is problem of practical and theoretical interest. However, traditional semi-supervised learning methods for image reconstruction often necessitate handcrafting a differentiable regularizer specific to some given imaging problem, which can be extremely time-consuming. In this work, we propose "supervision by denoising" (SUD), a framework to supervise reconstruction models using their own denoised output as labels. SUD unifies stochastic averaging and spatial denoising techniques under a spatio-temporal denoising framework and alternates denoising and model weight update steps in an optimization framework for semi-supervision. As example applications, we apply SUD to two problems from biomedical imaging--anatomical brain reconstruction (3D) and cortical parcellation (2D)--to demonstrate a significant improvement in reconstruction over supervised-only and ensembling baselines. While reconstruction models such as those based on the reconstruction network has proved extremely useful for U-Net [5] typically outperform handcrafted models in many imposing topological or spatial priors on the reconstruction imaging problems, they can involve millions of parameters [18], [19] and semi-supervised learning (SSL). SSL methods and, as a result, have a tendency to overfit training data and based on regularization suffer neither from limited diversity generalize poorly to previously unseen images at test time-- of augmented data nor domain gaps resulting from training a problem also exacerbated by distribution shift [6].

Federated learning (FL) goes beyond traditional, centralized machine learning by distributing model training among a large collection of edge clients. These clients cooperatively train a global, e.g., cloud-hosted, model without disclosing their local, private training data. The global model is then shared among all the participants which use it for local predictions. In this paper, we put forward a novel attacker model aiming at turning FL systems into covert channels to implement a stealth communication infrastructure. The main intuition is that, during federated training, a malicious sender can poison the global model by submitting purposely crafted examples. Although the effect of the model poisoning is negligible to other participants, and does not alter the overall model performance, it can be observed by a malicious receiver and used to transmit a single bit.

Bayesian networks (BNs) are a foundational model in machine learning and causal inference. Their graphical structure can handle high-dimensional problems, divide them into a sparse collection of smaller ones, underlies Judea Pearl's causality, and determines their explainability and interpretability. Despite their popularity, there are almost no resources in the literature on how to compute Shannon's entropy and the Kullback-Leibler (KL) divergence for BNs under their most common distributional assumptions. In this paper, we provide computationally efficient algorithms for both by leveraging BNs' graphical structure, and we illustrate them with a complete set of numerical examples. In the process, we show it is possible to reduce the computational complexity of KL from cubic to quadratic for Gaussian BNs.

Fox News chief national security correspondent Jennifer Griffin reports on shipping conglomerates rerouting their ships to avoid the Red Sea and U.S. Navy helicopters sinking three Houthi vessels on'Special Report.' A U.S.-led coalition of 13 countries issued a warning to Iran-backed Houthi rebels in Yemen on Wednesday, vowing consequences should the group continue attacks in the Red Sea. The U.S. was joined by the U.K., Australia, Bahrain, Belgium, Canada, Denmark, Germany, Italy, Japan, Netherlands and New Zealand in Wednesday's statement. The nations said that the destabilization of trade throughout the Red Sea is "unacceptable" and vowed consequences. "Ongoing Houthi attacks in the Red Sea are illegal, unacceptable, and profoundly destabilizing. There is no lawful justification for intentionally targeting civilian shipping and naval vessels. Attacks on vessels, including commercial vessels, using unmanned aerial vehicles, small boats, and missiles, including the first use of anti-ship ballistic missiles against such vessels, are a direct threat to the freedom of navigation that serves as the bedrock of global trade in one of the world's most critical waterways," the nations wrote.

Although Pilat works with the robots daily, she told The Guardian that she still doesn't fully understand them, so she works with Boston Dynamics engineers to shape the robot dogs' personalities. Together, Pilat and the engineers use AI, software, and machine learning to train the robots and even use Basia as a surrogate pet, frequently taking it for walks around New York. Basia is the'serious one,' Pilat told The Guardian, adding that the robot dog will paint about one canvas every three days, while Vanya is the'mother of the group' and paces around the studio. Meanwhile, Bunny's vanity often wins out, and it will continuously pose in front of a wall designed for selfies.

Inference and simulation in the context of high-dimensional dynamical systems remain computationally challenging problems. Some form of dimensionality reduction is required to make the problem tractable in general. In this paper, we propose a novel approximate Gaussian filtering and smoothing method which propagates low-rank approximations of the covariance matrices. This is accomplished by projecting the Lyapunov equations associated with the prediction step to a manifold of low-rank matrices, which are then solved by a recently developed, numerically stable, dynamical low-rank integrator. Meanwhile, the update steps are made tractable by noting that the covariance update only transforms the column space of the covariance matrix, which is low-rank by construction. The algorithm differentiates itself from existing ensemble-based approaches in that the low-rank approximations of the covariance matrices are deterministic, rather than stochastic. Crucially, this enables the method to reproduce the exact Kalman filter as the low-rank dimension approaches the true dimensionality of the problem. Our method reduces computational complexity from cubic (for the Kalman filter) to \emph{quadratic} in the state-space size in the worst-case, and can achieve \emph{linear} complexity if the state-space model satisfies certain criteria. Through a set of experiments in classical data-assimilation and spatio-temporal regression, we show that the proposed method consistently outperforms the ensemble-based methods in terms of error in the mean and covariance with respect to the exact Kalman filter. This comes at no additional cost in terms of asymptotic computational complexity.

Reservoir computing (RC) systems can efficiently forecast chaotic time series using nonlinear dynamical properties of an artificial neural network of random connections. The versatility of RC systems has motivated further research on both hardware counterparts of traditional RC algorithms and more efficient RC-like schemes. Inspired by the nonlinear processes in a living biological brain and using solitary waves excited on the surface of a flowing liquid film, in this paper we experimentally validate a physical RC system that substitutes the effect of randomness for a nonlinear transformation of input data. Carrying out all operations using a microcontroller with a minimal computational power, we demonstrate that the so-designed RC system serves as a technically simple hardware counterpart to the `next-generation' improvement of the traditional RC algorithm.

This paper explores the transformative potential of artificial intelligence (AI) in the context of sustainable agricultural development across diverse regions in Africa. Delving into opportunities, challenges, and impact, the study navigates through the dynamic landscape of AI applications in agriculture. Opportunities such as precision farming, crop monitoring, and climate-resilient practices are examined, alongside challenges related to technological infrastructure, data accessibility, and skill gaps. The article analyzes the impact of AI on smallholder farmers, supply chains, and inclusive growth. Ethical considerations and policy implications are also discussed, offering insights into responsible AI integration. By providing a nuanced understanding, this paper contributes to the ongoing discourse on leveraging AI for fostering sustainability in African agriculture.

Species distribution modeling is a highly versatile tool for understanding the intricate relationship between environmental conditions and species occurrences. However, the available data often lacks information on confirmed species absence and is limited to opportunistically sampled, presence-only observations. To overcome this limitation, a common approach is to employ pseudo-absences, which are specific geographic locations designated as negative samples. While pseudo-absences are well-established for single-species distribution models, their application in the context of multi-species neural networks remains underexplored. Notably, the significant class imbalance between species presences and pseudo-absences is often left unaddressed. Moreover, the existence of different types of pseudo-absences (e.g., random and target-group background points) adds complexity to the selection process. Determining the optimal combination of pseudo-absences types is difficult and depends on the characteristics of the data, particularly considering that certain types of pseudo-absences can be used to mitigate geographic biases. In this paper, we demonstrate that these challenges can be effectively tackled by integrating pseudo-absences in the training of multi-species neural networks through modifications to the loss function. This adjustment involves assigning different weights to the distinct terms of the loss function, thereby addressing both the class imbalance and the choice of pseudo-absence types. Additionally, we propose a strategy to set these loss weights using spatial block cross-validation with presence-only data. We evaluate our approach using a benchmark dataset containing independent presence-absence data from six different regions and report improved results when compared to competing approaches.

Using rules for home automation presents several challenges, especially when considering multiple stakeholders in addition to residents, such as homeowners, local authorities, energy suppliers, and system providers, who will wish to contribute rules to safeguard their interests. Managing rules from various sources requires a structured procedure, a relevant policy, and a designated authority to ensure authorized and correct contributions and address potential conflicts. In addition, the smart home rule language needs to express conditions and decisions at a high level of abstraction without specifying implementation details such as interfaces, access protocols, and room layout. Decoupling high-level decisions from these details supports the transferability and adaptability of rules to similar homes. This separation also has important implications for structuring the smart home system and the security architecture. Our proposed approach and system implementation introduce a rule management process, a rule administrator, and a domain-specific rule language to address these challenges. In addition, the system provides a learning process that observes residents, detects behavior patterns, and derives rules which are then presented as recommendations to the system.