Artificial intelligence (AI) models have been developed to predict clinically relevant biomarkers for colorectal cancer (CRC), including microsatellite instability (MSI). However, existing deep-learning networks are data-hungry and require large training datasets, which are often lacking in the medical domain. In this study, based on the latest Hierarchical Vision Transformer using Shifted Windows (Swin-T), we developed an efficient workflow for biomarkers in CRC (MSI, hypermutation, chromosomal instability, CpG island methylator phenotype, BRAF, and TP53 mutation) that required relatively small datasets, but achieved a state-of-the-art (SOTA) predictive performance. Our Swin-T workflow substantially outperformed published models in an intra-study cross-validation experiment using the TCGA-CRC-DX dataset (N = 462). It also demonstrated excellent generalizability in cross-study external validation and delivered a SOTA AUROC of 0.90 for MSI, using the MCO dataset for training (N = 1065) and the TCGA-CRC-DX for testing. A similar performance (AUROC = 0.91) was achieved by Echle et al., using ~8000 training samples (ResNet18) on the same testing dataset. Swin-T was extremely efficient when using small training datasets and exhibited robust predictive performance with 200-500 training samples. These data indicate that Swin-T could be 5-10 times more efficient than existing algorithms for MSI based on ResNet18 and ShuffleNet. Furthermore, the Swin-T models showed promise as pre-screening tests for MSI status and BRAF mutation status, which could exclude and reduce the samples before subsequent standard testing in a cascading diagnostic workflow, to allow a reduction in turnaround time and costs.

A social network (SN) is a social structure consisting of a group representing the interaction between them. SNs have recently been widely used and, subsequently, have become suitable and popular platforms for product promotion and information diffusion. People in an SN directly influence each other's interests and behavior. One of the most important problems in SNs is to find people who can have the maximum influence on other nodes in the network in a cascade manner if they are chosen as the seed nodes of a network diffusion scenario. Influential diffusers are people who, if they are chosen as the seed set in a publishing issue in the network, that network will have the most people who have learned about that diffused entity. This is a well-known problem in literature known as influence maximization (IM) problem. Although it has been proven that this is an NP-complete problem and does not have a solution in polynomial time, it has been argued that it has the properties of sub modular functions and, therefore, can be solved using a greedy algorithm. Most of the methods proposed to improve this complexity are based on the assumption that the entire graph is visible. However, this assumption does not hold for many real-world graphs. This study is conducted to extend current maximization methods with link prediction techniques to pseudo-visibility graphs. To this end, a graph generation method called the exponential random graph model (ERGM) is used for link prediction. The proposed method is tested using the data from the Snap dataset of Stanford University. According to the experimental tests, the proposed method is efficient on real-world graphs.

Earlier this year, scientists spotted the building blocks of RNA at the center of the Milky Way. RNA, or ribonucleic acid, a molecule similar to DNA and it is present in all living cells. The team of researchers discovered the building blocks of RNA in a molecular cloud in our galaxy. Such building blocks have also been discovered on asteroids. Most notably, Japanese researchers discovered more than 20 amino acids on the space rock Ryugu, which is more than 200 million miles (320 million kilometers) from Earth. Scientists made the detection by studying samples retrieved from the near-Earth asteroid by the Japan Aerospace Exploration Agency's (JAXA) Hayabusa2 spacecraft, which landed on Ryugu in 2018. According to Kensei Kobayashi, a professor emeritus of astrobiology at Yokohama National University, "Proving amino acids exist in the subsurface of asteroids increases the likelihood that the compounds arrived on Earth from space. This means that amino acids could likely be found on other planets and natural satellites – a clue that "life could have been born in more places in the Universe than previously thought," Building Blocks of Life Were Found on an Asteroid in Space For The Very First Time: ScienceAlert Victoria Meadows, Principal Investigator for NASA's Virtual Planetary Laboratory at the University of Washington has noted that life forms can produce detectable indicators, including the presence of substantial amounts of oxygen, smaller amounts of methane, and a variety of other chemicals. She believes that "upcoming telescopes in space and on the ground will have the capability to observe the atmospheres of Earth-sized planets orbiting nearby cool stars, so it's important to understand how best to recognize signs of habitability and life on these planets," Meadows said, "These computer models will help us determine whether an observed planet is more or less likely to support life."

Earlier this week an artificial intelligence-powered rapper was dropped from its label (yes, it had a label) after its algorithm learned to use racial slurs in its lyrics. More usefully, a recent AI trial at Queensland's Princess Alexandra Hospital was able to give early warnings as much as eight hours before a patient's condition was predicted to decline. Artificial technology is about to send a "tidal wave" of disruption through the way we work, according to a once-in-a-decade forecast by CSIRO, the national science agency. The federal government is being urged to use the upcoming national jobs summit to "double down" on policies set by the former government to ride that tidal wave, or risk being rode over. AI technology is forecast to replace as much as half of the work that is done today by 2030.

Every stage of life is impacted by mental health diseases, which range from dementia to schizophrenia. The World Health Organization estimates that one in eight people worldwide suffer from a mental condition and that poor mental health costs the world economy $1 trillion in lost productivity each year. Effective treatment for mental health illnesses depends on an early and precise diagnosis, just like it does for many illnesses. Nevertheless, unlike, for instance, a heart attack, which can be detected through tests that detect particular signs or "biomarkers" linked with the disorder, no clear-cut biomarkers for mental health issues have yet been identified. This is due to the intricate interplay of factors that causes mental diseases, such as heredity, biological predisposition, and unfavorable living circumstances.

Irony is a ubiquitous figurative language in daily communication. Previously, many researchers have approached irony from linguistic, cognitive science, and computational aspects. Recently, some progress have been witnessed in automatic irony processing due to the rapid development in deep neural models in natural language processing (NLP). In this paper, we will provide a comprehensive overview of computational irony, insights from linguistic theory and cognitive science, as well as its interactions with downstream NLP tasks and newly proposed multi-X irony processing perspectives.

Machine learning models are nowadays ubiquitous in space missions, performing a wide variety of tasks ranging from the prediction of multivariate time series through the detection of specific patterns in the input data. Adopted models are usually deep neural networks or other complex machine learning algorithms providing predictions that are opaque, i.e., human users are not allowed to understand the rationale behind the provided predictions. Several techniques exist in the literature to combine the impressive predictive performance of opaque machine learning models with human-intelligible prediction explanations, as for instance the application of symbolic knowledge extraction procedures. In this paper are reported the results of different knowledge extractors applied to an ensemble predictor capable of reproducing cosmic-ray data gathered on board the LISA Pathfinder space mission. A discussion about the readability/fidelity trade-off of the extracted knowledge is also presented.

Affirm is reinventing credit to make it more honest and friendly, giving consumers the flexibility to buy now and pay later without any hidden fees or compounding interest. We are looking for a Senior Engineer to lead projects and initiatives on our newest subteam within ML Platform: The ML Developer Productivity team. As an early team member, you will contribute extensively to setting and executing on a vision for increasing ML velocity at Affirm through a focus on developer productivity. Our mission is to build a self-service, easy-to-use foundation for developing and delivering robust models to production. This is a new team that will focus on creating internal tools used by ML Engineers for fast paced ML development.

Imagine if your smart speaker could be trained to recognize your accent, or if a pair of running shoes could alert you in real time if your gait changed, indicating fatigue. Or if, in the industrial world, sensors could parse vibration information from a machine that changed location and function often in real time, halting the machine if that information suggested there was a problem. We often write about the value of on-device machine learning (ML), but what we're generally discussing is running existing models on a device and matching incoming data against the established model. This is known as inference. So when you say the name "Alexa," your smart speaker matches the pattern and wakes up.

Every single data transformation we do in Artificial intelligence seeks to convert input-data to the most representative format required for the task we aim to solve… This conversion is done through functions. A machine-learning model transforms its input data into meaningful outputs. A process that is "learned" from exposure to known examples of inputs and outputs. Thus, the ML-model "learns a function" that maps its input data to the expected output. We have a table of a few data points, some belong to a "white" class and others to a "black" class.