Natural mortality factors for African White-backed Vultures in Namibia?

No Abstract. Vulture News Vol. 57 2007: pp. 62-6

Why do networks have inhibitory/negative connections?

Why do brains have inhibitory connections? Why do deep networks have negative weights? We propose an answer from the perspective of representation capacity. We believe representing functions is the primary role of both (i) the brain in natural intelligence, and (ii) deep networks in artificial intelligence. Our answer to why there are inhibitory/negative weights is: to learn more functions. We prove that, in the absence of negative weights, neural networks with non-decreasing activation functions are not universal approximators. While this may be an intuitive result to some, to the best of our knowledge, there is no formal theory, in either machine learning or neuroscience, that demonstrates why negative weights are crucial in the context of representation capacity. Further, we provide insights on the geometric properties of the representation space that non-negative deep networks cannot represent. We expect these insights will yield a deeper understanding of more sophisticated inductive priors imposed on the distribution of weights that lead to more efficient biological and machine learning.Comment: ICCV2023 camera-read

Prospectus, February 224, 1988

https://spark.parkland.edu/prospectus_1988/1006/thumbnail.jp

Prospectus, December 2, 1987

https://spark.parkland.edu/prospectus_1987/1027/thumbnail.jp

Prospectus, November 25, 1987

https://spark.parkland.edu/prospectus_1987/1026/thumbnail.jp

Prospectus, December 17, 1987

https://spark.parkland.edu/prospectus_1987/1029/thumbnail.jp

Feline low-grade alimentary lymphoma: an emerging entity and a potential animal model for human disease

Background: Low-grade alimentary lymphoma (LGAL) is characterised by the infiltration of neoplastic T-lymphocytes, typically in the small intestine. The incidence of LGAL has increased over the last ten years and it is now the most frequent digestive neoplasia in cats and comprises 60 to 75% of gastrointestinal lymphoma cases. Given that LGAL shares common clinical, paraclinical and ultrasonographic features with inflammatory bowel diseases, establishing a diagnosis is challenging. A review was designed to summarise current knowledge of the pathogenesis, diagnosis, prognosis and treatment of feline LGAL. Electronic searches of PubMed and Science Direct were carried out without date or language restrictions. Results: A total of 176 peer-reviewed documents were identified and most of which were published in the last twenty years. 130 studies were found from the veterinary literature and 46 from the human medicine literature. Heterogeneity of study designs and outcome measures made meta-analysis inappropriate. The pathophysiology of feline LGAL still needs to be elucidated, not least the putative roles of infectious agents, environmental factors as well as genetic events. The most common therapeutic strategy is combination treatment with prednisolone and chlorambucil, and prolonged remission can often be achieved. Developments in immunohistochemical analysis and clonality testing have improved the confidence of clinicians in obtaining a correct diagnosis between LGAL and IBD. The condition shares similarities with some diseases in humans, especially human indolent T-cell lymphoproliferative disorder of the gastrointestinal tract. Conclusions: The pathophysiology of feline LGAL still needs to be elucidated and prospective studies as well as standardisation of therapeutic strategies are needed. A combination of conventional histopathology and immunohistochemistry remains the current gold-standard test, but clinicians should be cautious about reclassifying cats previously diagnosed with IBD to lymphoma on the basis of clonality testing. Importantly, feline LGAL could be considered to be a potential animal model for indolent digestive T-cell lymphoproliferative disorder, a rare condition in human medicine