Women seeking a relationship have revealed which messages a prospective partner should definitely not send on dating apps. Researchers led by Purdue University, Indiana, found that among 275 participants -- mostly female -- starting a conversation with a sexually explicit message was the biggest turn-off, especially for people looking for a long-term relationship. Conversely, someone whose initial message included a greeting and a question was more likely to get a positive response. It comes as a separate group of scientists also revealed that tall men would prioritize tall women for relationships, but see short ones as just a fling. Women seeking a long-term relationship on dating apps find a sexually explicit opening line surprising and a violation, according to new research.

Professional movie critics aren't enjoying'Indiana Jones and the Dial of Destiny,' the hotly anticipated, and allegedly final, adventure for Harrison Ford as the whip-cracking archeologist. Pre-release reviews for the picture have lead to a'rotten' 50 percent score at Rotten Tomatoes, based on 46 reviews. And the aggregation site Metacritic gives the new Indy a score of 52/100, based on 24 reviews. But those failing grades could mean that'Indy 5' is shaping up to be a runaway summer sensation -- according to researchers at University of California Davis. Pre-release reviews for'Indiana Jones and the Dial of Destiny' have lead to a'rotten' score of 50% at Rotten Tomatoes and a 52/100 at Metacritic.

Recently, linear formulations and convex optimization methods have been proposed to predict diffusion-weighted Magnetic Resonance Imaging (dMRI) data given estimates of brain connections generated using tractography algorithms. The size of the linear models comprising such methods grows with both dMRI data and connectome resolution, and can become very large when applied to modern data. In this paper, we introduce a method to encode dMRI signals and large connectomes, i.e., those that range from hundreds of thousands to millions of fascicles (bundles of neuronal axons), by using a sparse tensor decomposition. We show that this tensor decomposition accurately approximates the Linear Fascicle Evaluation (LiFE) model, one of the recently developed linear models.

One-shot fine-grained visual recognition often suffers from the problem of training data scarcity for new fine-grained classes. To alleviate this problem, an off-the-shelf image generator can be applied to synthesize additional training images, but these synthesized images are often not helpful for actually improving the accuracy of one-shot fine-grained recognition. This paper proposes a meta-learning framework to combine generated images with original images, so that the resulting "hybrid" training images can improve one-shot learning. Specifically, the generic image generator is updated by a few training instances of novel classes, and a Meta Image Reinforcing Network (MetaIRNet) is proposed to conduct one-shot fine-grained recognition as well as image reinforcement. The model is trained in an end-to-end manner, and our experiments demonstrate consistent improvement over baselines on one-shot fine-grained image classification benchmarks.

Rising to meet the formidable challenge of the timely diagnosis of dementia, research scientists from Regenstrief Institute, IUPUI and the medical schools of Indiana University and University of Miami are conducting the Digital Detection of Dementia study, a real-world evaluation of the use of an artificial intelligence (AI) tool they developed for early identification of Alzheimer's disease and related dementias in primary care, the setting where most adults receive healthcare. Individuals identified as cognitively impaired will be referred for diagnostic services. The AI tool, called a passive digital marker, is a machine learning algorithm the researchers developed, trained and tested. The tool uses natural language processing to cull unstructured information in combination with structured data from a patient's electronic health record. These could include mention of memory issues, a notation of vascular concerns, comorbid conditions or other factors potentially linked to dementia. "Between 50 to 80 percent of dementia cases are unrecognized by the healthcare system in the U.S. And, if you include patients living with mild cognitive impairment, that number might actually climb to higher than 80 percent of cases that are not recognized," said Regenstrief Institute and Indiana University School of Medicine faculty member Malaz Boustani, M.D., MPH, senior author of the Digital Detection of Dementia study protocol paper, published in the peer-reviewed journal Trials.

Rising to meet the formidable challenge of the timely diagnosis of dementia, research scientists from Regenstrief Institute, IUPUI and the medical schools of Indiana University and University of Miami are conducting the Digital Detection of Dementia study, a real-world evaluation of the use of an artificial intelligence (AI) tool they developed for early identification of Alzheimer's disease and related dementias in primary care, the setting where most adults receive healthcare. Individuals identified as cognitively impaired will be referred for diagnostic services. The AI tool, called a passive digital marker, is a machine learning algorithm the researchers developed, trained and tested. The tool uses natural language processing to cull unstructured information in combination with structured data from a patient's electronic health record. These could include mention of memory issues, a notation of vascular concerns, comorbid conditions or other factors potentially linked to dementia. "Between 50 to 80 percent of dementia cases are unrecognized by the healthcare system in the U.S. And, if you include patients living with mild cognitive impairment, that number might actually climb to higher than 80 percent of cases that are not recognized," said Regenstrief Institute and Indiana University School of Medicine faculty member Malaz Boustani, M.D., MPH, senior author of the Digital Detection of Dementia study protocol paper, published in the peer reviewed journal Trials.

Scientists from the Regenstrief Institute, IUPUI, Indiana University, and the University of Miami are using Artificial Intelligence to identify undiagnosed cases of dementia in primary care settings as part of the Digital Detection of Dementia (D3) study. The study aimed to improve the timely diagnosis of dementia and provide diagnostic services to those who have been identified as cognitively impaired. The researchers developed an AI tool called a Passive Digital Marker, which uses a machine learning algorithm and natural language processing to analyze a patient's electronic health record. The tool combines structured data, such as notes about memory problems or vascular issues, with unstructured information to identify potential indicators of dementia. Unfortunately, many people with ADRD go undiagnosed, and even when a diagnosis is made, it often comes 2 to 5 years after the onset of symptoms, when the disease is already in the mild to moderate stage.

For this smaller group of patients, physicians may have limited clinical decision-making experience or evidence-based guidance for choosing drug combinations. The solution is to expand the number of patients to support development of general principles to guide decision-making. Combining patient data from multiple healthcare institutions, however, requires deep expertise in artificial intelligence (AI) and wide-ranging experience in developing machine learning models using sensitive and complex healthcare data. Hitachi, U of U Health, and Regenstrief researchers partnered to develop and test a new AI method that analyzed electronic health record data across Utah and Indiana and learned generalizable treatment patterns of type 2 diabetes patients with similar characteristics. Those patterns can now be used to help determine an optimal drug regimen for a specific patient.

This story was originally published by the Guardian and is reproduced here as part of the Climate Desk collaboration. There are an estimated 73,300 species of tree on Earth, 9,000 of which have yet to be discovered, according to a global count of tree species by thousands of researchers who used second world war codebreaking techniques created at Bletchley Park to evaluate the number of unknown species. Researchers working on the ground in 90 countries collected information on 38 million trees, sometimes walking for days and camping in remote places to reach them. The study found there are about 14 percent more tree species than previously reported and that a third of undiscovered tree species are rare, meaning they could be vulnerable to extinction by human-driven changes in land use and the climate crisis. "It is a massive effort for the whole world to document our forests," said Jingjing Liang, a lead author of the paper and professor of quantitative forest ecology at Purdue University in Indiana, US. "Counting the number of tree species worldwide is like a puzzle with pieces spreading all over the world. We solved it together as a team, each sharing our own piece."

We introduce Ego4D, a massive-scale egocentric video dataset and benchmark suite. It offers 3,025 hours of daily-life activity video spanning hundreds of scenarios (household, outdoor, workplace, leisure, etc.) captured by 855 unique camera wearers from 74 worldwide locations and 9 different countries. The approach to collection is designed to uphold rigorous privacy and ethics standards with consenting participants and robust de-identification procedures where relevant. Ego4D dramatically expands the volume of diverse egocentric video footage publicly available to the research community. Portions of the video are accompanied by audio, 3D meshes of the environment, eye gaze, stereo, and/or synchronized videos from multiple egocentric cameras at the same event. Furthermore, we present a host of new benchmark challenges centered around understanding the first-person visual experience in the past (querying an episodic memory), present (analyzing hand-object manipulation, audio-visual conversation, and social interactions), and future (forecasting activities). By publicly sharing this massive annotated dataset and benchmark suite, we aim to push the frontier of first-person perception. Project page: https://ego4d-data.org/