25 research outputs found
The Light We Give: Sikh Wisdom for Cultivating Empathy and Justice
No full textGrowing up in South Texas, Dr. Simran Jeet Singh and his brothers confronted racism daily. As a turbaned, bearded, brown-skinned Sikh, he continued to face prejudice and hate in college and beyond. Simran chose to be defined not by the negativity that often surrounded him but by the Sikh teachings of love and justice that he grew up with. Delving deep into these core tenets of Sikh wisdom, he has sought to embrace an outlook that guides us to see the good in everyone and to forge a path of positivity, connection, and service—a way of life that so many of us are seeking in today’s world.
We all say that we choose love over hate. But when tested, we realize that it’s easier said than done and that our empathy for others is not rooted deeply enough. As a turbaned and bearded Sikh man, Simran has been subjected to racism his whole life. He has been working on the frontlines of hate violence for more than a decade. And yet, he has managed to avoid falling into the toxic trap of hate and anger. In this lecture, drawing on his recent book The Light We Give, he will draw from his personal experiences and from hate incidents he has witnessed firsthand to share the wisdom he has gained on what it really takes to choose love over hate.
Simran Jeet Singh, Ph.D., is the Executive Director of the Religion & Society Program at the Aspen Institute and the author of the national bestseller The Light We Give: How Sikh Wisdom Can Transform Your Life (Riverhead, Penguin Random House). Simran\u27s thought leadership on bias, empathy, and justice extends across corporate, university, and government settings. He is an Atlantic Fellow for Racial Equity with Columbia University and the Nelson Mandela Foundation, a Soros Equality Fellow with the Open Society Foundations, a Visiting Lecturer at Union Seminary, and a Senior Advisor on Equity and Inclusion for YSC Consulting, part of Accenture.
Organized and hosted by the Interfaith Fellows Program of the Jay Phillips Center for Interreligious Studies at the University of St. Thomas and the Minnesota Multifaith Network in collaboration with the Lutheran Center for Faith, Values, and Community at St. Olaf College and the Interfaith Institute at Augsburg University. Cosponsored by Minnesota Multifaith Network, and the Office of Diversity, Equity and Inclusion, the College of Arts and Sciences, the Diversity Activities Board (DAB), and the Department of Theology at the University of St. Thomas. Funded, in part, by generous grants from the Arthur Vining Davis Foundations, the Jay and Rose Phillips Family Foundation of Minnesota, and the Center for Faculty Development at the University of St. Thomas
Keynote Address: The Light We Give: Sikh Wisdom for Cultivating Empathy and Justice
No full textGrowing up in South Texas, Dr. Simran Jeet Singh and his brothers confronted racism daily. As a turbaned, bearded, brown-skinned Sikh, he continued to face prejudice and hate in college and beyond. Simran chose to be defined not by the negativity that often surrounded him but by the Sikh teachings of love and justice that he grew up with. Delving deep into these core tenets of Sikh wisdom, he has sought to embrace an outlook that guides us to see the good in everyone and to forge a path of positivity, connection, and service—a way of life that so many of us are seeking in today’s world.
We all say that we choose love over hate. But when tested, we realize that it’s easier said than done and that our empathy for others is not rooted deeply enough. As a turbaned and bearded Sikh man, Simran has been subjected to racism his whole life. He has been working on the frontlines of hate violence for more than a decade. And yet, he has managed to avoid falling into the toxic trap of hate and anger. In this lecture, drawing on his recent book The Light We Give, he will draw from his personal experiences and from hate incidents he has witnessed firsthand to share the wisdom he has gained on what it really takes to choose love over hate.
Simran Jeet Singh, Ph.D., is the Executive Director of the Religion & Society Program at the Aspen Institute and the author of the national bestseller The Light We Give: How Sikh Wisdom Can Transform Your Life (Riverhead, Penguin Random House). Simran\u27s thought leadership on bias, empathy, and justice extends across corporate, university, and government settings. He is an Atlantic Fellow for Racial Equity with Columbia University and the Nelson Mandela Foundation, a Soros Equality Fellow with the Open Society Foundations, a Visiting Lecturer at Union Seminary, and a Senior Advisor on Equity and Inclusion for YSC Consulting, part of Accenture.
Organized and hosted by the Interfaith Fellows Program of the Jay Phillips Center for Interreligious Studies at the University of St. Thomas and the Minnesota Multifaith Network in collaboration with the Lutheran Center for Faith, Values, and Community at St. Olaf College and the Interfaith Institute at Augsburg University. Cosponsored by Minnesota Multifaith Network, and the Office of Diversity, Equity and Inclusion, the College of Arts and Sciences, and the Department of Theology at the University of St. Thomas. the , and in collaboration with the Office of Diversity, Equity and Inclusion at the University of St. Thomas. Funded, in part, by generous grants from the Arthur Vining Davis Foundations, the Jay and Rose Phillips Family Foundation of Minnesota, and the Center for Faculty Development at the University of St. Thomas
Deep Transfer Learning for Intelligent Autonomous Vehicles
No full textAutonomous driving has become a very interesting research problem for the deep learning domain. While Intelligent Autonomous Vehicles (IAVs) have developed significantly over the last 10 years, there are still unresolved issues concerning how to transfer knowledge from one driving environment to another. In particular, there is hardly anything known about how to get IAVs trained for driving on one side of the road (e.g., left-hand side in New Zealand and Japan) to right-hand side (e.g., the USA and China). This research describes how a deep learning IAV lane-positioning model can predict the steering angle based on continuous left-hand drive images and velocity inputs for 50 minutes of simulated driving (over 32,000 images) using convolutional neural networks (CNNs). We then examine freezing weights at different layers for successful transfer to right-hand simulated driving (10 minutes and over 7,000 images) and find that the best layers to freeze lie closest to the output layer. By visualizing the effects of weights at different levels, we report that the model shows signs of extracting increasingly relevant features at the higher levels that may help to explain how human drivers transfer knowledge about how to drive on one side of the road to the other. The overall contribution of this thesis is showing how a deep learning IAV model can adhere to lane-positioning by predicting the steering angle and can also transfer knowledge from left hand to right hand drive simulated driving
Deep Transfer Learning for Intelligent Autonomous Vehicles
No full textAutonomous driving has become a very interesting research problem for the deep learning domain. While Intelligent Autonomous Vehicles (IAVs) have developed significantly over the last 10 years, there are still unresolved issues concerning how to transfer knowledge from one driving environment to another. In particular, there is hardly anything known about how to get IAVs trained for driving on one side of the road (e.g., left-hand side in New Zealand and Japan) to right-hand side (e.g., the USA and China). This research describes how a deep learning IAV lane-positioning model can predict the steering angle based on continuous left-hand drive images and velocity inputs for 50 minutes of simulated driving (over 32,000 images) using convolutional neural networks (CNNs). We then examine freezing weights at different layers for successful transfer to right-hand simulated driving (10 minutes and over 7,000 images) and find that the best layers to freeze lie closest to the output layer. By visualizing the effects of weights at different levels, we report that the model shows signs of extracting increasingly relevant features at the higher levels that may help to explain how human drivers transfer knowledge about how to drive on one side of the road to the other. The overall contribution of this thesis is showing how a deep learning IAV model can adhere to lane-positioning by predicting the steering angle and can also transfer knowledge from left hand to right hand drive simulated driving
Statin-induced deficits in memory and learning: A behavioural and electrophysiological investigation
No full textStatins play a crucial role in reducing the risk of death from cardiovascular disease in millions of people worldwide. Recently, data show people taking statins are at increased risk of a number of psychiatric adverse events such as amnesia, anxiety and even aggression. However, there are conflicting epidemiological data and a scarcity of direct experimental evidence that statins can alter neural functioning.
This thesis aimed to investigate the effect of statin treatment on memory in an animal model of spatial memory and learning; the Morris Water maze (MWM) using guinea pigs. The behavioural results demonstrate that statins, independent of their musculoskeletal or liver adverse effects, significantly induced deficits in specific aspects of the MWM. Statins at a clinically equivalent dose of 20mg/d did not affect reference memory directly by affecting latency or distance to platform, but resulted in increased thigmotactic activity. Further behavioural investigations using a higher dose and modified protocol showed once again that statins did not affect spatial reference memory; however, statin treatment for six weeks induced deficits in spatial working memory (short-term memory).
Mechanisms of memory have been hypothesised to result from changes in synaptic plasticity in the hippocampus. Extracellular field recordings of synaptic transmission in area CA1 of hippocampal slices were conducted to assess the effects of statin application on LTP. Statins significantly reduced the amount of LTP expressed in a dose-dependent manner. Further investigations with methyl-beta-cyclodextrin (MBCD), a compound that sequesters cholesterol from lipid membranes, demonstrated that statins act independently of cholesterol reduction to decrease the expression of LTP. Furthermore, statins did not affect paired pulse facilitation, but induction of LTP reduced the paired pulse ratio (PPR) in statin-treated slices. These results therefore suggest that statins may induce paired pulse depression after the induction of LTP.
To assess these findings further and provide clinical consensus, the effect of six weeks of chronic statin administration on LTP was investigated. Hippocampal slices from statin-treated animals showed reduced expression of LTP compared with vehicle treated animals; however, this was not statistically significant. It is possible that behavioural training prior to electrophysiological assessment could have obscured from detection of any deficits in LTP following chronic statin treatment. To further investigate the molecular mechanisms of statin-induced deficits, western blot analysis of GluN1 and GluA1, specific subunits of glutamatergic receptors known to be critically involved in memory and learning were assessed. Statins induced a 1.5 fold increase in GluA1 but did not affect the expression of GluN1.
In conclusion, the results of this thesis demonstrate that statins administered to healthy guinea pigs, at clinically relevant doses, can induce specific behavioural deficits in spatial memory. Furthermore, statins attenuated hippocampal LTP (in vitro), independently of their cholesterol-lowering properties. This thesis has taken the first step in providing evidence to suggest how statins may induce deficits in memory and learning, and in the process has led to new understandings of the role of statins in hippocampal synaptic plasticity, memory and learning
Statin-induced deficits in memory and learning: A behavioural and electrophysiological investigation
No full textStatins play a crucial role in reducing the risk of death from cardiovascular disease in millions of people worldwide. Recently, data show people taking statins are at increased risk of a number of psychiatric adverse events such as amnesia, anxiety and even aggression. However, there are conflicting epidemiological data and a scarcity of direct experimental evidence that statins can alter neural functioning.
This thesis aimed to investigate the effect of statin treatment on memory in an animal model of spatial memory and learning; the Morris Water maze (MWM) using guinea pigs. The behavioural results demonstrate that statins, independent of their musculoskeletal or liver adverse effects, significantly induced deficits in specific aspects of the MWM. Statins at a clinically equivalent dose of 20mg/d did not affect reference memory directly by affecting latency or distance to platform, but resulted in increased thigmotactic activity. Further behavioural investigations using a higher dose and modified protocol showed once again that statins did not affect spatial reference memory; however, statin treatment for six weeks induced deficits in spatial working memory (short-term memory).
Mechanisms of memory have been hypothesised to result from changes in synaptic plasticity in the hippocampus. Extracellular field recordings of synaptic transmission in area CA1 of hippocampal slices were conducted to assess the effects of statin application on LTP. Statins significantly reduced the amount of LTP expressed in a dose-dependent manner. Further investigations with methyl-beta-cyclodextrin (MBCD), a compound that sequesters cholesterol from lipid membranes, demonstrated that statins act independently of cholesterol reduction to decrease the expression of LTP. Furthermore, statins did not affect paired pulse facilitation, but induction of LTP reduced the paired pulse ratio (PPR) in statin-treated slices. These results therefore suggest that statins may induce paired pulse depression after the induction of LTP.
To assess these findings further and provide clinical consensus, the effect of six weeks of chronic statin administration on LTP was investigated. Hippocampal slices from statin-treated animals showed reduced expression of LTP compared with vehicle treated animals; however, this was not statistically significant. It is possible that behavioural training prior to electrophysiological assessment could have obscured from detection of any deficits in LTP following chronic statin treatment. To further investigate the molecular mechanisms of statin-induced deficits, western blot analysis of GluN1 and GluA1, specific subunits of glutamatergic receptors known to be critically involved in memory and learning were assessed. Statins induced a 1.5 fold increase in GluA1 but did not affect the expression of GluN1.
In conclusion, the results of this thesis demonstrate that statins administered to healthy guinea pigs, at clinically relevant doses, can induce specific behavioural deficits in spatial memory. Furthermore, statins attenuated hippocampal LTP (in vitro), independently of their cholesterol-lowering properties. This thesis has taken the first step in providing evidence to suggest how statins may induce deficits in memory and learning, and in the process has led to new understandings of the role of statins in hippocampal synaptic plasticity, memory and learning
Seven Daily Practices to Imbibe Wismad in Our Lives
No full textWismad is a central concept in Sikh philosophy, referring to a profound state of awe, wonder, and reverential amazement experienced in the awareness of the Divine and the vastness of creation. It is not mere surprise or emotional excitement, but a deep existential and spiritual orientation in which the ego recedes, and the individual becomes receptive to the Infinite. Wismad is cultivated through mindfulness, Naam Simran, and attunement to the Divine presence within and beyond creation
“Disco Dreads” Self-fashioning through Consumption in Uganda’s Hip Hop Scene:Image-making, Branding and Belonging in Fragile Sites
No full textThis thesis explores interactions between self-fashioning and consumption in the hip hop scene in Uganda. In the performances of musical and social life, hip hop reveals first, tactile interactions between ideas and objects, subjective and relational to processes of consumption and production. Second, these interactions find expression through enactments of activism and hedonism, aspects which hip hop as global cultural product consistently and problematically engages with. This acts as a critical space to contemplate wider social formations and historical processes. In this thesis, such interactions are interrogated through aspects of self-fashioning, mapped through the visual in the form of image and brand showing how these preoccupations, while a first glance divergent, come from a place of belonging, which is wanting a better life.This project is an interdisciplinary one. Choosing a visual focus with which to engage with this sonic culture, I rely on ethnographic data gathered in the field combined with digital ethnography. I draw on scholarship from ethnomusicology, popular music studies, media and cultural studies and perspectives from post-colonial studies to reveal a dialogue between plenitude and paucity. This is a conversation informed by the images and imagery of hip hop, its music, its media narratives and mythologies, set against a backdrop of deep socio-economic inequity and thus, profound fragility.<br/
Do increased levels of progesterone and progesterone/estradiol ratio on the day of human chorionic gonadotropin affects pregnancy outcome in long agonist protocol in fresh in vitro fertilization/intracytoplasmic sperm injection cycles?
No full textBackground: The effect of elevated levels of serum progesterone (P 4 ) and estradiol (E 2 ) on the day of human chorionic gonadotropin and their cut-off value on in vitro fertilization (IVF) outcomes is still not clear. Aims: The aim was to evaluate the association between serum P 4 , E 2 and progesterone/estradiol ratio (P 4 /E 2 ) on pregnancy outcome in IVF/intracytoplasmic sperm injection (ICSI) cycles with long agonist protocol. Setting and Design: Retrospective, single center, cohort study. Materials and Methods: A review of complete data of 544 women undergoing fresh IVF/ICSI cycles (539 cycles) with long agonist protocol from January 2012 to February 2014 was done. Data were stratified into Three groups according to the number of oocytes retrieved: low (≤4 oocytes obtained), intermediate (5-19 oocytes obtained), and high ovarian response (≥20 oocytes obtained). Statistical Analysis: Fishers exact test/Chi-square was carried for comparing categorical data. Receiver operating characteristics analysis was performed to determine the cut-off value for P 4 and P 4 /E 2 detrimental for pregnancy. Results: A negative association was observed between pregnancy rate (PR) and serum P 4 and P 4 /E 2 levels with no effect on fertilization and cleavage rate. The overall cut-off value of serum P 4 and P 4 /E 2 ratio detrimental for pregnancy was found to be 1.075 and ≥0.35, respectively. Different P 4 threshold according to the ovarian responders were calculated, 1.075 for intermediate and 1.275 for high responders. Serum E 2 levels were not found to be significantly associated with PR. Conclusion: Serum P 4 levels and P 4 /E 2 ratio are a significant predictor for pregnancy outcome without affecting cleavage and fertilization rate while serum estradiol levels do not seem to affect PR
Aerial 360-Degree Video Delivery for Immersive First Person View UAV Navigation
No full textAdaptive transmission of conventional video from a UAV to the ground has been researched for various applications, but the research topic of 360° video transmission from a UAV for the specific application of first-person view (FPV) based navigation is still nascent. In this work, we present adaptive 360° video compression and streaming methods to optimize the perceptual quality of experience of a pilot, who navigates the UAV in real time by viewing this immersive FPV feed, which is sent wirelessly from the UAV to the pilot. This adaptation of the 360° FPV feed is performed in response to the wireless channel conditions and the pilot\u27s viewport, wherein each 360° frame is split into two regions of variable size, one meant to be within the pilot\u27s viewport and the other outside. Each region is encoded using different H. 265 quantization parameters (QP) and modulation orders. We model the scenario realistically by generating probability distributions of the variation in frame size and quality with QP, for aerial 360° videos. These models are expressed using a two-term exponential function, whose parameters are also provided. This model achieves lower prediction errors than the single-term exponential and power law functions. Simulations on a set of aerial 360-degree videos demonstrate that the adaptive approach achieves 9.73 dB (21.77 %) greater QoE than a baseline approach that utilizes throughput-based adaptive bit rate algorithm (ABR) to tune QP per GoP, and a 5G new radio adaptive modulation scheme (AMS) to tune modulation order: Additionally, we present a deep reinforcement learning approach to adapt FPV, which achieves an expected pilot QoE just 2.07 dB lower than the adaptive approach, while being significantly faster and requiring no prior knowledge of the environment
