The insects build differently when exposed to a pathogen. Breakthroughs, discoveries, and DIY tips sent every weekday. When the COVID-19 pandemic struck, we had to completely reorganize our spaces to avoid close contact. Transparent barriers were erected between seats, cashiers and customers, receptionists and patients, while stickers encouraged people to sit or stand at least six feet away from each other. A new study, however, reveals that we're not the only ones who take such actions to lessen the spread of a disease.

In today's vast literature landscape, a manual review is very time-consuming. To address this challenge, this paper proposes a semi-automated tool for solution method review and selection. It caters to researchers, practitioners, and decision-makers while serving as a benchmark for future work. The tool comprises three modules: (1) paper selection and scoring, using a keyword selection scheme to query Scopus API and compute relevancy; (2) solution method extraction in papers utilizing OpenAI API; (3) sensitivity analysis and post-analyzes. It reveals trends, relevant papers, and methods. AI in the oncology case study and several use cases are presented with promising results, comparing the tool to manual ground truth.

We propose a method for recovering the structure of a sparse undirected graphical model when very few samples are available. The method decides about the presence or absence of bonds between pairs of variable by considering one pair at a time and using a closed form formula, analytically derived by calculating the posterior probability for every possible model explaining a two body system using Jeffreys prior. The approach does not rely on the optimization of any cost functions and consequently is much faster than existing algorithms. Despite this time and computational advantage, numerical results show that for several sparse topologies the algorithm is comparable to the best existing algorithms, and is more accurate in the presence of hidden variables. We apply this approach to the analysis of US stock market data and to neural data, in order to show its efficiency in recovering robust statistical dependencies in real data with non-stationary correlations in time and/or space.

Sticking to one task at a time is an increasing rarity, as things such as checking social media while watching TV, or online shopping while on the phone become the norm. But if you have ever talked to a friend or family member while tapping out emoji on WhatsApp, only to realise you've missed half the conversation, you'll know how juggling tasks can cause you to lose focus. Now, a new poll has found that constant multitasking may actually be hindering our performance, reducing focus for parents and children. Research from the US suggests that constant multitasking may actually be hindering our performance, reducing focus for parents and children alike. While tech has an increasingly important role in modern life, a poll of 1,200 parents and teens in the US found the constant multitasking and omnipresence of tech is leading to an'always on' mentality.

We consider different types of single-hidden-Iayer feedforward nets: with or without direct input to output connections, and using either threshold or sigmoidal activation functions. The main results show that direct connections in threshold nets double the recognition but not the interpolation power, while using sigmoids rather than thresholds allows (at least) doubling both. Various results are also given on VC dimension and other measures of recognition capabilities.

We consider different types of single-hidden-Iayer feedforward nets: with or without direct input to output connections, and using either threshold or sigmoidal activation functions. The main results show that direct connections in threshold nets double the recognition but not the interpolation power, while using sigmoids rather than thresholds allows (at least) doubling both. Various results are also given on VC dimension and other measures of recognition capabilities.

We consider different types of single-hidden-Iayer feedforward nets: with or without direct input to output connections, and using either threshold orsigmoidal activation functions. The main results show that direct connections in threshold nets double the recognition but not the interpolation power,while using sigmoids rather than thresholds allows (at least) doubling both. Various results are also given on VC dimension and other measures of recognition capabilities.