19 research outputs found
UATTA-EB: Uncertainty-Aware Test-Time Augmented Ensemble of BERTs for Classifying Common Mental Illnesses on Social Media Posts
Given the current state of the world, because of existing situations around
the world, millions of people suffering from mental illnesses feel isolated and
unable to receive help in person. Psychological studies have shown that our
state of mind can manifest itself in the linguistic features we use to
communicate. People have increasingly turned to online platforms to express
themselves and seek help with their conditions. Deep learning methods have been
commonly used to identify and analyze mental health conditions from various
sources of information, including social media. Still, they face challenges,
including a lack of reliability and overconfidence in predictions resulting in
the poor calibration of the models. To solve these issues, We propose UATTA-EB:
Uncertainty-Aware Test-Time Augmented Ensembling of BERTs for producing
reliable and well-calibrated predictions to classify six possible types of
mental illnesses- None, Depression, Anxiety, Bipolar Disorder, ADHD, and PTSD
by analyzing unstructured user data on Reddit.Comment: Accepted at Tiny Papers @ ICLR 202
2D GEOMETRIC SHAPE AND COLOR RECOGNITION USING DIGITAL IMAGE PROCESSING
ABSTRACT: The paper discusses an approach involving digital image processing and geometric logic for recognition of two dimensional shapes of objects such as squares, circles, rectangles and triangles as well as the color of the object. This approach can be extended to applications like robotic vision and computer intelligence. The methods involved are three dimensional RGB image to two dimensional black and white image conversion, color pixel classification for object-background separation, area based filtering and use of bounding box and its properties for calculating object metrics. The object metrics are compared with predetermined values that are characteristic of a particular object's shape. The recognition of the shape of the objects is made invariant to their rotation. Further, the colors of the objects are recognized by analyzing RGB information of all pixels within each object. The algorithm was developed and simulated using MATLAB. A set of 180 images of the four basic 2D geometric shapes and the three primary colors (red, green and blue) were used for analysis and the results were 99% accurate
Can NLP Models 'Identify', 'Distinguish', and 'Justify' Questions that Don't have a Definitive Answer?
Though state-of-the-art (SOTA) NLP systems have achieved remarkable
performance on a variety of language understanding tasks, they primarily focus
on questions that have a correct and a definitive answer. However, in
real-world applications, users often ask questions that don't have a definitive
answer. Incorrectly answering such questions certainly hampers a system's
reliability and trustworthiness. Can SOTA models accurately identify such
questions and provide a reasonable response?
To investigate the above question, we introduce QnotA, a dataset consisting
of five different categories of questions that don't have definitive answers.
Furthermore, for each QnotA instance, we also provide a corresponding QA
instance i.e. an alternate question that ''can be'' answered. With this data,
we formulate three evaluation tasks that test a system's ability to 'identify',
'distinguish', and 'justify' QnotA questions. Through comprehensive
experiments, we show that even SOTA models including GPT-3 and Flan T5 do not
fare well on these tasks and lack considerably behind the human performance
baseline. We conduct a thorough analysis which further leads to several
interesting findings. Overall, we believe our work and findings will encourage
and facilitate further research in this important area and help develop more
robust models.Comment: TrustNLP Workshop at ACL 202
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Not AvailableNeural stem cells (NSCs) can self-renew and give rise to neurons, astrocytes and oligodendrocytes; they are found in the nervous system of mammalian organisms, representing a promising resource for both fundamental research and therapeutics. There have been few investigations on NSCs in the livestock species. Therefore, we have successfully isolated and characterised NSCs from the foetal brain of a small domestic animal, the goat (called GNSCs). These cells from the foetal brain showed self-renewal, rapid proliferation with a population doubling time of 88 h, were morphologically homogeneous and maintained normal chromosome throughout the culture period. The cells expressed NSC-specific markers (Sox2, Pax6 and Mushashi), but were negative for CD34 and CD45. They were capable of multi-differentiation into neurons, astrocytes, oligodendrocytes, as well as adipocytes and osteocytes. The availability of such cells may hold great interest for basic and applied neuroscience.Not Availabl