Pratul dash biography templates
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SSDM Time-Accurate Speech Rich Transcription with Non-Fluencies
Jiachen Lian, Xuanru Zhou, Zoe Ezzes, Jet Vonk, Brittany Morin, David Baquirin,
Zachary Miller, Maria Luisa Gorno Tempini, Gopala Anumanchipalli
UC Berkeley, Zhejiang University, UCSF
{jiachenlian, gopala}@
Abstract
Speech is a hierarchical collection of ord, prosody, emotions, dysfluencies, etc. Automatic transcription of speech that goes beyond skrivelse (words) fryst vatten an underexplored problem. We focus on transcribing speech along with non-fluencies (dysfluencies). The current state-of-the-art pipeline (Lian et al., ) suffers from complex architecture design, training complexity, and significant shortcomings in the local sequence aligner, and it does not explore in-context learning capacity. In this work, we propose SSDM , which tackles those shortcomings via four main contributions: (1) We föreslå a novel neural articulatory flow to derive highly scalable speech representations. (2) We developed a full-stack connec
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Individual Assignment 1
Individual Assignment 1
PAY EQUITY
You are aware from reading the Wilson Bros Case Scenario that the company has operations in the
Province of Ontario. As a result Pay Equity will be a major component of your strategy going forward,
in particular for the employees in Ontario.
1-Explain the notion of Equal Pay for Equal Work. What legislation contains this notion? (5 Marks)
Ans: Equal Pay for Equal Work - Section 42 of the Employment Standards Act (Ontario) makes it
illegal to pay employees of one sex less money for doing the same work as employees of the other sex:
No employer shall pay an employee of one sex at a rate of pay less than the rate paid to an employee
of the other sex when:
a. They perform substantially the same kind of work in the same establishment
b. Their performance requires substantially the same skill, effort and responsibility
c. Their work is performed under similar conditions
(Filsinger, pg. ) There can be exceptions in this pay only if based on
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Medical imaging tasks require an understanding of subtle and localized visual features due to the inherently detailed and area-specific nature of pathological patterns, which are crucial for clinical diagnosis. Although recent advances in medical vision-language pre-training (VLP) enable models to learn clinically relevant visual features by leveraging both medical images and their associated radiology reports, current medical VLP methods primarily focus on aligning images with entire reports. This focus hinders the learning of dense (pixel-level) visual features and is suboptimal for dense prediction tasks (e.g., medical image segmentation).To address this challenge, we propose a novel medical VLP framework, named Global to Dense level representation learning (G2D), which aims to learn global and dense visual features simultaneously using only image-text pairs without extra annotations. In particular, G2D designs a Pseudo Segmentation (PS) task, which enables the model to lear