Signed graphs are an emergent way of representing data in a variety of contexts were conflicting interactions exist. These include data from biological, ecological, and social systems. Here we propose the concept of communicability geometry for signed graphs, proving that metrics in this space, such as the communicability distance and angles, are Euclidean and spherical. We then apply these metrics to solve several problems in data analysis of signed graphs in a unified way. They include the partitioning of signed graphs, dimensionality reduction, finding hierarchies of alliances in signed networks as well as the quantification of the degree of polarization between the existing factions in systems represented by this type of graphs.

The increasing utilization of emerging technologies in the Food & Agriculture (FA) sector has heightened the need for security to minimize cyber risks. Considering this aspect, this manuscript reviews disclosed and documented cybersecurity incidents in the FA sector. For this purpose, thirty cybersecurity incidents were identified, which took place between July 2011 and April 2023. The details of these incidents are reported from multiple sources such as: the private industry and flash notifications generated by the Federal Bureau of Investigation (FBI), internal reports from the affected organizations, and available media sources. Considering the available information, a brief description of the security threat, ransom amount, and impact on the organization are discussed for each incident. This review reports an increased frequency of cybersecurity threats to the FA sector. To minimize these cyber risks, popular cybersecurity frameworks and recent agriculture-specific cybersecurity solutions are also discussed. Further, the need for AI assurance in the FA sector is explained, and the Farmer-Centered AI (FCAI) framework is proposed. The main aim of the FCAI framework is to support farmers in decision-making for agricultural production, by incorporating AI assurance. Lastly, the effects of the reported cyber incidents on other critical infrastructures, food security, and the economy are noted, along with specifying the open issues for future development.

Yield forecasting, the science of predicting agricultural productivity before the crop harvest occurs, helps a wide range of stakeholders make better decisions around agricultural planning. This study aims to investigate whether machine learning-based yield prediction models can capably predict Kharif season rice yields at the district level in India several months before the rice harvest takes place. The methodology involved training 19 machine learning models such as CatBoost, LightGBM, Orthogonal Matching Pursuit, and Extremely Randomized Trees on 20 years of climate, satellite, and rice yield data across 247 of Indian rice-producing districts. In addition to model-building, a dynamic dashboard was built understand how the reliability of rice yield predictions varies across districts. The results of the proof-of-concept machine learning pipeline demonstrated that rice yields can be predicted with a reasonable degree of accuracy, with out-of-sample R2, MAE, and MAPE performance of up to 0.82, 0.29, and 0.16 respectively. These results outperformed test set performance reported in related literature on rice yield modeling in other contexts and countries. In addition, SHAP value analysis was conducted to infer both the importance and directional impact of the climate and remote sensing variables included in the model. Important features driving rice yields included temperature, soil water volume, and leaf area index. In particular, higher temperatures in August correlate with increased rice yields, particularly when the leaf area index in August is also high. Building on the results, a proof-of-concept dashboard was developed to allow users to easily explore which districts may experience a rise or fall in yield relative to the previous year.

We introduce a surrogate-based black-box optimization method, termed Polynomial-model-based optimization (PMBO). The algorithm alternates polynomial approximation with Bayesian optimization steps, using Gaussian processes to model the error between the objective and its polynomial fit. We describe the algorithmic design of PMBO and compare the results of the performance of PMBO with several optimization methods for a set of analytic test functions. The results show that PMBO outperforms the classic Bayesian optimization and is robust with respect to the choice of its correlation function family and its hyper-parameter setting, which, on the contrary, need to be carefully tuned in classic Bayesian optimization. Remarkably, PMBO performs comparably with state-of-the-art evolutionary algorithms such as the Covariance Matrix Adaptation -- Evolution Strategy (CMA-ES). This finding suggests that PMBO emerges as the pivotal choice among surrogate-based optimization methods when addressing low-dimensional optimization problems. Hereby, the simple nature of polynomials opens the opportunity for interpretation and analysis of the inferred surrogate model, providing a macroscopic perspective on the landscape of the objective function.

Applications that deal with sensitive information may have restrictions placed on the data available to a machine learning (ML) classifier. For example, in some applications, a classifier may not have direct access to sensitive attributes, affecting its ability to produce accurate and fair decisions. This paper proposes a framework that models the trade-off between accuracy and fairness under four practical scenarios that dictate the type of data available for analysis. Prior works examine this trade-off by analyzing the outputs of a scoring function that has been trained to implicitly learn the underlying distribution of the feature vector, class label, and sensitive attribute of a dataset. In contrast, our framework directly analyzes the behavior of the optimal Bayesian classifier on this underlying distribution by constructing a discrete approximation it from the dataset itself. This approach enables us to formulate multiple convex optimization problems, which allow us to answer the question: How is the accuracy of a Bayesian classifier affected in different data restricting scenarios when constrained to be fair? Analysis is performed on a set of fairness definitions that include group and individual fairness. Experiments on three datasets demonstrate the utility of the proposed framework as a tool for quantifying the trade-offs among different fairness notions and their distributional dependencies.

This paper introduces AL$\ell_0$CORE, a new form of probabilistic non-negative tensor decomposition. AL$\ell_0$CORE is a Tucker decomposition where the number of non-zero elements (i.e., the $\ell_0$-norm) of the core tensor is constrained to a preset value $Q$ much smaller than the size of the core. While the user dictates the total budget $Q$, the locations and values of the non-zero elements are latent variables and allocated across the core tensor during inference. AL$\ell_0$CORE -- i.e., $allo$cated $\ell_0$-$co$nstrained $core$-- thus enjoys both the computational tractability of CP decomposition and the qualitatively appealing latent structure of Tucker. In a suite of real-data experiments, we demonstrate that AL$\ell_0$CORE typically requires only tiny fractions (e.g.,~1%) of the full core to achieve the same results as full Tucker decomposition at only a correspondingly tiny fraction of the cost.

The Yemen-based branch of Al Qaeda said on Sunday that its leader, Khaled Batarfi, had died. Al Qaeda in the Arabian Peninsula, known as A.Q.A.P., released a video announcing Mr. Batarfi's death, showing images of him wrapped in a white funeral shroud overlaid with a black Al Qaeda flag. It did not explain how he had died. The United States government once considered Al Qaeda in the Arabian Peninsula to be one of the world's most dangerous terrorist organizations. The group tried and failed at least three times to blow up American airliners, and has been targeted by American drone strikes for two decades.

If you're looking for an image that perfectly showcases the confusion and chaos of a choose-your-own-reality information dystopia, you probably couldn't do better than yesterday's portrait of Catherine, Princess of Wales. In just one day, the photograph has transformed from a hastily released piece of public-relations damage control into something of a Rorschach test--a collision between plausibility and conspiracy. For the uninitiated: Yesterday, in celebration of Mother's Day in the U.K., the Royal Family released a portrait on Instagram of Kate Middleton with her three children. But this was no ordinary photo. Middleton has been away from the public eye since December reportedly because of unspecified health issues, leading to a ceaseless parade of conspiracy theories. Royal watchers and news organizations naturally pored over the image, and they found a number of alarming peculiarities.

Everyone from influencers to celebrities - and now, even the British monarchy - have been caught altering their photos shared online. Experts say it is becoming increasingly difficult with AI and photo-editing apps - but there are still 10 giveaways that you should be aware of. These include checking the edges of people and objects, investigating shadows and inspecting the background for curves. The tips comes as multiple major news agencies withdrew Kensington Palace's first photo of Princess of Wales Kate Middleton on Sunday, after picture editors noticed at least 16 different details that did not look right. Kensington Palace yesterday released the first picture of the Princess of Wales since surgery. Major news agencies quickly pulled the photo after editors noticed more than a dozen details that suggested it had been altered.

Joseph Humire, of the Center for a Secret Free Society, joined'Fox & Friends Weekend' to discuss reports that Chinese migrant encounters are skyrocketing under Biden. The United States is constantly assessing the need to expand export controls to stop China from acquiring advanced computer chips and manufacturing equipment that could be used to boost its military, U.S. Commerce Secretary Gina Raimondo said Monday. The U.S. export controls were first launched in 2022 to counter the use of chips for military applications that include the development of hypersonic missiles and artificial intelligence. Last year, the U.S. Commerce Department broadened the export controls, sparking protests from China's Commerce Ministry that the restrictions violated international trade rules and "seriously threaten the stability of industrial supply chains." China said it would take "all necessary measures" to safeguard its rights and interests and urged Washington to lift the export control as soon as possible. Asked if the U.S. was planning to further broaden the chip export controls to China, Raimondo said in a news conference in the Philippine capital Manila that it was constantly under consideration.