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Researchers have developed an algorithm that may determine the essential wants of customers from the textual content and pictures they share on social networks. The consultants hope this software will assist psychologists to diagnose potential psychological well being issues. The research means that Spanish-speaking customers usually tend to point out relationship issues when feeling depressed than English audio system.

We spend a considerable quantity of our time sharing pictures, movies or ideas on social networks comparable to Instagram, Fb and Twitter. Now, a bunch of researchers from the Universitat Oberta de Catalunya (UOC) has developed an algorithm that goals to assist psychologists diagnose potential psychological well being issues by means of the content material folks put up on these platforms.

In accordance with William Glasser’s Alternative Principle, there are 5 which are central to all human conduct: Survival, Energy, Freedom, Belonging and Enjoyable. These wants even have an affect on the photographs we select to add to our Instagram web page. “How we current ourselves on can present helpful details about behaviors, personalities, views, motives and wishes,” defined Mohammad Mahdi Dehshibi, who led this research throughout the AI for Human Properly-being (AIWELL) group, which belongs to the College of Pc Science, Multimedia and Telecommunications on the UOC.

The analysis group has spent two years engaged on a that identifies the 5 wants described by Glasser, utilizing multimodal knowledge comparable to pictures, textual content, biography and geolocation. For the research, which has been printed within the journal IEEE Transactions on Affective Computing, 86 Instagram profiles, in each Spanish and Persian, have been analyzed.

Drawing on and databases, the consultants skilled an algorithm to determine the content material of the photographs and to categorize textual content material by assigning totally different labels proposed by psychologists, who in contrast the outcomes with a database containing over 30,000 pictures, captions and feedback.

The issue of standardizing the labels obtained from texts and pictures was solved with a codebook, Bag-of-Content material, which they described as a “semantic map from the visible to the textual area.” In accordance with the researchers, the experiments “present promising accuracy and complementary data between visible and textual cues.”

Does every alternative we make reply to only one fundamental want? Glasser’s idea says in any other case, and the multi-label method of this research is beneficial in clearing up this doubt. Dehshibi, presently a analysis scientist at Universidad Carlos III de Madrid’s (UC3M) imBody reasearch laboratory and on the Unconventional Computing Laboratory, UWE Bristol, makes use of an instance to elucidate this: “Think about {that a} bike owner is driving up a mountain, and on the high, they’ll select between sharing a selfie and a bunch photograph. In the event that they select the selfie, we understand a necessity for Energy, but when they select the opposite possibility, we will conclude that the particular person shouldn’t be solely on the lookout for Enjoyable but additionally a approach to fulfill their want for Belonging.”

As well as, the truth that the profiles analyzed belong to individuals who talk in two totally different languages avoids cultural bias. Earlier research have discovered, for instance, that Spanish-speaking customers usually tend to point out when they’re feeling depressed than English audio system. “Finding out knowledge from social networks that belong to non-English talking customers may assist construct inclusive and numerous instruments and fashions for addressing psychological well being issues in folks with numerous cultural or linguistic backgrounds,” they famous.

The authors consider that their analysis can assist enhance , starting from identification to improved therapy when an individual has been identified with a psychological well being dysfunction.


Bot can spot depressed Twitter users in 9 out of 10 cases


Extra data:
Mohammad Mahdi Dehshibi et al, A deep multimodal studying method to understand fundamental wants of people from Instagram profile, IEEE Transactions on Affective Computing (2021). DOI: 10.1109/TAFFC.2021.3090809

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Machine Learning

A novel AI algorithm for digital pathology analysis

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Digital pathology is an emerging field which deals mainly with microscopy images that are derived from patient biopsies. Because of the high resolution, most of these whole slide images (WSI) have a large size, typically exceeding a gigabyte (Gb). Therefore, typical image analysis methods cannot efficiently handle them.

Seeing a need, researchers from Boston University School of Medicine (BUSM) have developed a novel artificial intelligence (AI) algorithm based on a framework called representation learning to classify lung cancer subtype based on lung tissue images from resected tumors.

“We are developing novel AI-based methods that can bring efficiency to assessing digital pathology data. Pathology practice is in the midst of a digital revolution. Computer-based methods are being developed to assist the expert pathologist. Also, in places where there is no expert, such methods and technologies can directly assist diagnosis,” explains corresponding author Vijaya B. Kolachalama, Ph.D., FAHA, assistant professor of medicine and at BUSM.

The researchers developed a graph-based vision transformer for digital pathology called Graph Transformer (GTP) that leverages a graph representation of pathology images and the computational efficiency of transformer architectures to perform analysis on the whole slide image.

“Translating the latest advances in computer science to digital pathology is not straightforward and there is a need to build AI methods that can exclusively tackle the problems in digital pathology,” explains co-corresponding author Jennifer Beane, Ph.D., associate professor of medicine at BUSM.

Using whole slide images and from three publicly available national cohorts, they then developed a model that could distinguish between lung adenocarcinoma, lung , and adjacent non-cancerous tissue. Over a series of studies and sensitivity analyses, they showed that their GTP framework outperforms current state-of-the-art methods used for whole slide image classification.

They believe their machine learning framework has implications beyond digital . “Researchers who are interested in the development of computer vision approaches for other real-world applications can also find our approach to be useful,” they added.

These findings appear online in the journal IEEE Transactions on Medical Imaging.


Automated assessment of pathology image quality


More information:
Yi Zheng et al, A graph-transformer for whole slide image classification, IEEE Transactions on Medical Imaging (2022). DOI: 10.1109/TMI.2022.3176598

Citation:
A novel AI algorithm for digital pathology analysis (2022, May 23)
retrieved 23 May 2022
from https://techxplore.com/news/2022-05-ai-algorithm-digital-pathology-analysis.html

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Machine Learning

Neuromorphic memory device simulates neurons and synapses

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Neuromorphic memory device simulates neurons and synapses

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Neuromorphic reminiscence system consisting of backside unstable and high nonvolatile reminiscence layers emulating neuronal and synaptic properties, respectively. Credit score: The Korea Superior Institute of Science and Know-how (KAIST)

Researchers have reported a nano-sized neuromorphic reminiscence system that emulates neurons and synapses concurrently in a unit cell, one other step towards finishing the aim of neuromorphic computing designed to carefully mimic the human mind with semiconductor gadgets.

Neuromorphic computing goals to understand (AI) by mimicking the mechanisms of neurons and that make up the . Impressed by the cognitive features of the human mind that present computer systems can’t present, neuromorphic gadgets have been extensively investigated. Nonetheless, present Complementary Steel-Oxide Semiconductor (CMOS)-based neuromorphic circuits merely join synthetic neurons and synapses with out synergistic interactions, and the concomitant implementation of neurons and synapses nonetheless stays a problem. To handle these points, a analysis crew led by Professor Keon Jae Lee from the Division of Supplies Science and Engineering applied the organic working mechanisms of people by introducing the neuron-synapse interactions in a single reminiscence cell, somewhat than the standard strategy of electrically connecting synthetic neuronal and synaptic gadgets.

Just like industrial graphics playing cards, the substitute synaptic gadgets beforehand studied typically used to speed up parallel computations, which exhibits clear variations from the operational mechanisms of the human mind. The analysis crew applied the synergistic interactions between neurons and synapses within the neuromorphic reminiscence system, emulating the mechanisms of the organic neural community. As well as, the developed neuromorphic system can exchange advanced CMOS neuron circuits with a single system, offering excessive scalability and value effectivity.

The human mind consists of a posh community of 100 billion neurons and 100 trillion synapses. The features and buildings of neurons and synapses can flexibly change in line with the exterior stimuli, adapting to the encompassing surroundings. The analysis crew developed a neuromorphic system during which short-term and long-term reminiscences coexist utilizing unstable and non-volatile reminiscence gadgets that mimic the traits of neurons and synapses, respectively. A threshold change system is used as and phase-change reminiscence is used as a non-volatile system. Two thin-film gadgets are built-in with out intermediate electrodes, implementing the practical adaptability of neurons and synapses within the neuromorphic reminiscence.

Neuromorphic memory device simulates neurons and synapses
Retraining operation within the neuromorphic system array. a) Schematic graph exhibiting the retraining impact. b) Scanning electron microscope picture of the neuromorphic system array. c) Coaching sample “F” for the retraining take a look at. d) Evolution of the reminiscence state of the neuromorphic system array for the naive coaching and retraining scheme. Credit score: The Korea Superior Institute of Science and Know-how (KAIST)

Professor Keon Jae Lee defined, “Neurons and synapses work together with one another to determine cognitive features equivalent to reminiscence and studying, so simulating each is a vital component for brain-inspired synthetic intelligence. The developed neuromorphic system additionally mimics the retraining impact that permits fast studying of the forgotten info by implementing a constructive suggestions impact between and synapses.”

This consequence, titled “Simultaneous emulation of synaptic and intrinsic plasticity utilizing a memristive synapse,” was printed within the Could 19, 2022 situation of Nature Communications.


Team presents brain-inspired, highly scalable neuromorphic hardware


Extra info:
Sang Hyun Sung et al, Simultaneous emulation of synaptic and intrinsic plasticity utilizing a memristive synapse, Nature Communications (2022). DOI: 10.1038/s41467-022-30432-2

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Neuromorphic reminiscence system simulates neurons and synapses (2022, Could 20)
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Machine Learning

Neural network to help ecologists, foresters and power line operators size up trees from above

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Neural network to help ecologists, foresters and power line operators size up trees from above

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Experiment workflow for cover top estimation by utilization of deep neural networks, completely different configurations of enter knowledge, varied spectral compound, spatial decision and topography options. The dotted strains present non-obligatory steps for enter tensor creation. Credit score: IEEE Entry (2022). DOI: 10.1109/ACCESS.2022.3161568

Skoltech researchers have skilled a neural mannequin to find out the peak of timber on satellite tv for pc imagery to observe the pure surroundings, infrastructure, and timber provide. Not like prior options, the mannequin presented in IEEE Entry doesn’t require drone footage or imaging past the seen vary, in a number of infrared bands—such knowledge are a lot more durable to come back by.

Environmental scientists, , and folks from the wooden business want dependable estimates of forest cover top primarily based on optical . It tells them how nicely the ecosystem is doing, how a lot atmospheric carbon dioxide the timber are capturing, whether or not there’s danger of timber damaging , and the way a lot timber is offered for logging.

Observations on location by forest rangers or utilizing drones aren’t possible for big areas and areas which might be laborious to entry. The choice is satellite tv for pc imaging concurrently with a number of cameras that span a number of bands of infrared radiation along with seen gentle. These so-called multispectral knowledge are extra scarce and costly, although.

In a not too long ago printed examine, Skoltech researchers and their colleague from Sber current a that delivers high-quality predictions of forest cover top utilizing strange, optical satellite tv for pc imagery solely.

“The one largest issue that makes our neural community profitable is its capacity to investigate spatial knowledge and texture traits. It exploits the connection that exists between tree crown form and top,” says the primary writer of the examine, Skoltech Ph.D. pupil Svetlana Illarionova.

“Our neural community attracts on extra enter knowledge,” she continues. “Together with the optical imagery, we put in supplementary options within the type of ArcticDEM, a freely accessible high-resolution mannequin. It’s a 2-meter-resolved illustration of the naked topographic floor of the Earth masking boreal areas.”

The for the mannequin come from the northern Russian area of Arkhangelsk. Cover top predictions are scored primarily based on how nicely they match lidar observations made on location in that area with drones. That mentioned, the researchers say their answer is relevant to wherever roughly comparable vegetation grows.


Neural network has learned to identify tree species


Extra data:
Svetlana Illarionova et al, Estimation of the Cover Peak Mannequin From Multispectral Satellite tv for pc Imagery With Convolutional Neural Networks, IEEE Entry (2022). DOI: 10.1109/ACCESS.2022.3161568

Quotation:
Neural community to assist ecologists, foresters and energy line operators measurement up timber from above (2022, Might 20)
retrieved 20 Might 2022
from https://techxplore.com/information/2022-05-neural-network-ecologists-foresters-power.html

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