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The IAAF’s hyperandrogenism regulations suspended
No full textOn 27 July, the Court of Arbitration for Sport (‘CAS’) delivered a landmark ruling on the regulation of gender in sport. The decision explores how the categorisation of sport on the basis of sex can be best reconciled with the “biological reality” that human sex cannot necessarily be divided so clearly. Dr. Seema Patel, Senior Lecturer at Nottingham Trent University, Deputy Director of the Centre for Sports Law and author of ‘Inclusion and Exclusion in Competitive Sport: Socio-Legal and Regulatory Perspectives,’ reviews the case and suggests that sport regulation must be cautious of traditional criteria to determine eligibility in sports
Numerical simulation and modeling of microdroplet evaporation
No full textSubmission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-11-11 without embargo termsThe student, Ankit Patel, accepted the attached license on 2022-04-20 at 15:06.The student, Ankit Patel, submitted this Thesis for approval on 2022-04-20 at 15:10.This Thesis was approved for publication on 2022-04-27 at 16:12.DSpace SAF Submission Ingestion Package generated from Vireo submission #17858 on 2022-11-11 at 13:42:36The ability to understand and quantify droplet evaporation and its associated heat and mass transport is extremely valuable to a variety of industries such as, and not limited to, combustion, energy systems, computer technology, thermal management systems, and advanced manufacturing. The primary reason for this value is derived from the ability for droplets to absorb high heat fluxes across relatively small length scales and efficiently transport that energy by using their latent heat of vaporization. With this intrinsic value proposition, it is imperative to be able to numerically model and predict this complex phenomenon to design and invent solutions that will help drive a variety of industries forward. In this thesis, a numerical model is developed which is capable of prediction evaporation of liquid microdroplets. Using previously established and verified experimental results, various numerical models and simulation approaches were investigated and the results ultimately compared to determine the most optimal numerical modeling methodology. Ultimately, the work completed within this thesis serves as a foundation for future studies into the complex physical mechanisms governing evaporation and condensation at the liquid-vapor interfaces
Author interview: Q and A with Dr Ian Sanjay Patel on we’re here because you were there: immigration and the end of empire
No full textIn this author interview, we speak to Dr Ian Sanjay Patel about his new book, We’re Here Because You Were There: Immigration and the End of Empire, which explores post-war immigration laws, the afterlives of British imperial citizenship and related attempts to reimagine and rejuvenate British imperialism after 1945. Contributing to transnational histories of decolonisation, the book also explores the interconnections between human rights, post-war migration and international diplomacy. Author Interview with Dr Ian Sanjay Patel, author of We’re Here Because You Were There: Immigration and the End of Empire. Verso. 2021
Neurally plausible mechanisms for learning selective and invariant representations
No full textAbstract
Coding for visual stimuli in the ventral stream is known to be invariant to object identity preserving nuisance transformations. Indeed, much recent theoretical and experimental work suggests that the main challenge for the visual cortex is to build up such nuisance invariant representations. Recently, artificial convolutional networks have succeeded in both learning such invariant properties and, surprisingly, predicting cortical responses in macaque and mouse visual cortex with unprecedented accuracy. However, some of the key ingredients that enable such success—supervised learning and the backpropagation algorithm—are neurally implausible. This makes it difficult to relate advances in understanding convolutional networks to the brain. In contrast, many of the existing neurally plausible theories of invariant representations in the brain involve unsupervised learning, and have been strongly tied to specific plasticity rules. To close this gap, we study an instantiation of simple-complex cell model and show, for a broad class of unsupervised learning rules (including Hebbian learning), that we can learn object representations that are invariant to nuisance transformations belonging to a finite orthogonal group. These findings may have implications for developing neurally plausible theories and models of how the visual cortex or artificial neural networks build selectivity for discriminating objects and invariance to real-world nuisance transformations
Some psychological issues of HIV/AIDS patients: a core analysis of adherence
No full textAnkit Patel , Prof. Suresh Makvan
Embedded in the Body: the Poetry, History and Politics of Migritude with Shailja Patel (2021-02-25)
No full textOnline discussion, reading and Q&A; Thursday, February 25 at 4:00PM CST; Shailja Patel is the bestselling author of Migritude, taught in over 100 colleges and universities worldwide. Patel's poems have been translated into 17 languages, and been featured in the Smithsonian. The Nobel Women's Initiative honored her with a Global Feminist Spotlight. She is currently a Research Associate at Five College Women's Studies Research Center.Women, Gender & Sexuality Studies program; Alworth Institute for International Studies; Department of Anthropology, Sociology & Criminology; English program; Writing Studies programPatel, Shailja. (2021). Embedded in the Body: the Poetry, History and Politics of Migritude with Shailja Patel (2021-02-25). Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/220654
Supplemental Material - Ketamine in Critically Ill Patients: Use, Perceptions, and Potential Barriers
No full textSupplemental Material for Ketamine in Critically Ill Patients: Use, Perceptions, and Potential Barriers by Carolyn M. Bell, Megan A. Rech, Kwame Akuamoah-Boateng, George Kasotakis, Jeffrey D. McMurray, Benjamin A. Moses, Scott W. Mueller, Gourang P. Patel, Russel J. Roberts, Ankit Sakhuja, Ann Salvator, Erika L. Setliff, Christopher A. Droege in Journal of Pharmacy Practice</p
The Patel trials: further evidence of the need to reform the Griffith Codes
No full textThis article argues that the two trials of Dr Jayant Patel for criminal medical negligence under s 288 of the Criminal Code 1899 Act (Qld) highlight the inadequacies of the duty provisions in the Griffith Codes of Queensland and Western Australia. The difficulties with these duty provisions extend beyond causation and go to the heart of the construction of the Griffith Codes. The fundamental problem lies in the wording of s 23 of both the Queensland and
the Western Australia Codes, the principal section dealing with criminal responsibility, which allows a prosecution for criminal negligence under two alternative routes with different standards of proof, and the importation of
common law criminal negligence into the duty provisions in the absence of a specified fault element in the relevant Code sections. It is further contended that other criminal law jurisdictions in Australia, such as the Criminal Code
1995 (Cth), offer a better model for the prosecution of criminal negligence cases that flow from breach of a specified duty. The article has greatly benefited from comments provided to the author by Justice HG Fryberg, who
conducted the second Patel trial
Translational Symmetry in Convolutions With Localized Kernels Causes an Implicit Bias Toward High Frequency Adversarial Examples
No full textAdversarial attacks are still a significant challenge for neural networks. Recent efforts have shown that adversarial perturbations typically contain high-frequency features, but the root cause of this phenomenon remains unknown. Inspired by theoretical work on linear convolutional models, we hypothesize that translational symmetry in convolutional operations together with localized kernels implicitly bias the learning of high-frequency features, and that this is one of the main causes of high frequency adversarial examples. To test this hypothesis, we analyzed the impact of different choices of linear and non-linear architectures on the implicit bias of the learned features and adversarial perturbations, in spatial and frequency domains. We find that, independently of the training dataset, convolutional operations have higher frequency adversarial attacks compared to other architectural parameterizations, and that this phenomenon is exacerbated with stronger locality of the kernel (kernel size) end depth of the model. The explanation for the kernel size dependence involves the Fourier Uncertainty Principle: a spatially-limited filter (local kernel in the space domain) cannot also be frequency-limited (local in the frequency domain). Using larger convolution kernel sizes or avoiding convolutions (e.g., by using Vision Transformers or MLP-style architectures) significantly reduces this high-frequency bias. Looking forward, our work strongly suggests that understanding and controlling the implicit bias of architectures will be essential for achieving adversarial robustness
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