Cardiac imaging known as echocardiography is a non-invasive tool utilized to produce data including images and videos, which cardiologists use to diagnose cardiac abnormalities in general and myocardial infarction (MI) in particular. Echocardiography machines can deliver abundant amounts of data that need to be quickly analyzed by cardiologists to help them make a diagnosis and treat cardiac conditions. However, the acquired data quality varies depending on the acquisition conditions and the patient's responsiveness to the setup instructions. These constraints are challenging to doctors especially when patients are facing MI and their lives are at stake. In this paper, we propose an innovative real-time end-to-end fully automated model based on convolutional neural networks (CNN) to detect MI depending on regional wall motion abnormalities (RWMA) of the left ventricle (LV) from videos produced by echocardiography. Our model is implemented as a pipeline consisting of a 2D CNN that performs data preprocessing by segmenting the LV chamber from the apical four-chamber (A4C) view, followed by a 3D CNN that performs a binary classification to detect if the segmented echocardiography shows signs of MI. We trained both CNNs on a dataset composed of 165 echocardiography videos each acquired from a distinct patient. The 2D CNN achieved an accuracy of 97.18% on data segmentation while the 3D CNN achieved 90.9% of accuracy, 100% of precision and 95% of recall on MI detection. Our results demonstrate that creating a fully automated system for MI detection is feasible and propitious.

Maintaining farm sustainability through optimizing the agricultural management practices helps build more planet-friendly environment. The emerging satellite missions can acquire multi- and hyperspectral imagery which captures more detailed spectral information concerning the scanned area, hence allows us to benefit from subtle spectral features during the analysis process in agricultural applications. We introduce an approach for extracting 2.5 m cultivated land maps from 10 m Sentinel-2 multispectral image series which benefits from a compact graph convolutional neural network. The experiments indicate that our models not only outperform classical and deep machine learning techniques through delivering higher-quality segmentation maps, but also dramatically reduce the memory footprint when compared to U-Nets (almost 8k trainable parameters of our models, with up to 31M parameters of U-Nets). Such memory frugality is pivotal in the missions which allow us to uplink a model to the AI-powered satellite once it is in orbit, as sending large nets is impossible due to the time constraints.

Dynamical systems theory has recently been applied in optimization to prove that gradient descent algorithms avoid so-called strict saddle points of the loss function. However, in many modern machine learning applications, the required regularity conditions are not satisfied. In particular, this is the case for rectified linear unit (ReLU) networks. In this paper, we prove a variant of the relevant dynamical systems result, a center-stable manifold theorem, in which we relax some of the regularity requirements. Then, we verify that shallow ReLU networks fit into the new framework. Building on a classification of critical points of the square integral loss of shallow ReLU networks measured against an affine target function, we deduce that gradient descent avoids most saddle points. We proceed to prove convergence to global minima if the initialization is sufficiently good, which is expressed by an explicit threshold on the limiting loss.

The implementation of energy communities represents a cross-disciplinary phenomenon that has the potential to support the energy transition while fostering citizens' participation throughout the energy system and their exploitation of renewables. An important role is played by online information sources in engaging people in this process and increasing their awareness of associated benefits. In this view, this work analyses online news data on energy communities to understand people's awareness and the media importance of this topic. We use the Semantic Brand Score (SBS) indicator as an innovative measure of semantic importance, combining social network analysis and text mining methods. Results show different importance trends for energy communities and other energy and society-related topics, also allowing the identification of their connections. Our approach gives evidence to information gaps and possible actions that could be taken to promote a low-carbon energy transition.

Abstract--With the development of blockchain technology, the cryptocurrency based on blockchain technology is becoming more and more popular. This gave birth to a huge cryptocurrency transaction network has received widespread attention. Link prediction learning structure of network is helpful to understand the mechanism of network, so it is also widely studied in cryptocurrency network. However, the dynamics of cryptocurrency transaction networks have been neglected in the past researches. We use graph regularized method to link past transaction records with future transactions. Based on this, we propose a single latent factor-dependent, non-negative, multiplicative and graph regularized-incorporated update (SLF-NMGRU) algorithm and further propose graph regularized nonnegative latent factor analysis (GrNLFA) model.

IPIs caused by protozoan and helminth parasites are among the most common infections in humans in LMICs. They are regarded as a severe public health concern, as they cause a wide array of potentially detrimental health conditions. Researchers have been developing pattern recognition techniques for the automatic identification of parasite eggs in microscopic images. Existing solutions still need improvements to reduce diagnostic errors and generate fast, efficient, and accurate results. Our paper addresses this and proposes a multi-modal learning detector to localize parasitic eggs and categorize them into 11 categories. The experiments were conducted on the novel Chula-ParasiteEgg-11 dataset that was used to train both EfficientDet model with EfficientNet-v2 backbone and EfficientNet-B7+SVM. The dataset has 11,000 microscopic training images from 11 categories. Our results show robust performance with an accuracy of 92%, and an F1 score of 93%. Additionally, the IOU distribution illustrates the high localization capability of the detector.

During his long career as a polemicist and a strategist of terror, Ayman al-Zawahiri often taunted the United States. He hewed to the familiar theme that America was an apostate power at war with Islam. But he also described it as a spent force. In a video released this spring, he said that "U.S. weakness" was responsible for the war triggered by Russia's invasion of Ukraine, and he mocked the country's standing "after its defeat in Iraq and Afghanistan, after the economic disasters caused by the 9/11 invasions, after the coronavirus pandemic, and after it left its ally Ukraine as prey for the Russians." The U.S. drone strike in Kabul last Saturday that killed Zawahiri, who was seventy-one, added a punctuation mark to the long search for justice for the victims of 9/11 and of other deadly attacks that Zawahiri directly approved, such as the bombing of two U.S. Embassies in Africa in 1998, which killed twelve Americans and more than two hundred Africans.

At first glance, the July 31 killing of al-Qaeda chief Ayman al-Zawahiri by a US drone attack in Kabul, Afghanistan, appears to be the most significant setback the group has experienced since the death of its founder, Osama bin Laden, in 2011. However, throughout the decade he administered al-Qaeda, al-Zawahiri worked to ensure the organisation has all the necessary tools in place to survive his death. As such, while the operation that eliminated one of the organisers of the 9/11 attacks is undoubtedly a major win for the current US administration, it is unlikely to debilitate the group. Indeed, the fallout from this targeted assassination will be minimal for al-Qaeda. Al-Zawahiri, seen by many as nothing other than a "grey bureaucrat", can easily be replaced by someone with a similar managerial mindset.

The future looks bright, perhaps thanks to the three Suns? These past few days, we've been tinkering with two AI engines. DALL·E 2 is an artificial intelligence system that can create realistic images and art from a description in natural language. It is part of a larger OpenAI set of models. Midjourney is a similar AI program that creates images from textual descriptions.

In his short address, delivered on a White House balcony with the monuments behind him, the president vowed not to permit another sanctuary for terrorism. "We will never again, never again allow Afghanistan to become a terrorist safe haven, because he is gone and we're going to make sure nothing else happens," he said. "It can't be a launching pad against the United States. We're going to see to it that won't happen." While celebrating al-Zawahri's killing, Republicans wasted little time on Monday night asserting that the president's withdrawal had endangered the country.