54 research outputs found
After midnight
No full textThis collection of stories follows a multi-generational family from India to the United States, and explores how the effect of migration, loss, shame, and nationhood is passed down through generations.M.F.A.by Aarti Monteir
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In Conversation with Aarti Shahani
No full textThis flyer promotes "In Conversation with Aarti Shahani," an event where author Aarti Shahani discusses her book Here We Are: American Dreams, American Nightmares.Asian American Studie
Odour-background segregation and source localisation using fast olfactory processing
No full textThe hungry insect relies on olfaction to find food patches. However the natural environment is full of different odours made up of a variety of odorants intermingling together. So how can the insect recognise which odorants belong to a patch of good food that is worth visiting? Segregating appetitive food odorants poses a difficult challenge for the insect, as it must separate target odorants from mixtures of odorants that come together from a variety of sources, a process called odour background segregation. During flight, the insect can use spatial and temporal information in turbulent odour plumes to determine whether odorants come from one source or multiple sources. The insect olfactory system can process odorants rapidly, matching the resolution of other senses such as vision and audition. When the insect arrives at an odour source, it must forage alongside other insects to localise the odour to its source and find its food reward. The insect could potentially gain information from other nearby conspecifics that would increase its success in foraging. Thus, during the search for food, the insect olfactory system must process information from odour plume structure and odour valence, from memories about good food sources and from social information transfer between conspecifics. This thesis concentrates on what information insects use during olfactory search to locate a food source and the neural mechanisms that convert an olfactory stimulus into a behavioural response. Two of the chapters focus on odour background segregation, primarily what temporal information insects (honey bees and fruit flies) can use to segregate an appetitive odorant from a background odour mixture. The last chapter focuses on social information transfer between insects (fruit flies) at an odorous food source, and whether such information can improve foraging. Firstly, I asked whether insects were able to distinguish a target odorant from an odour background using odorant onset asynchrony, when the insect had never experienced this target odorant alone. I addressed this question using Apis mellifera, the honey bee, by appetitively conditioning fixed honey bees to a target odorant while presenting a complex background mixture. In my first chapter, I demonstrated that honey bees could separate an unknown target odorant from a mixture using odorant onset asynchrony, however the onset asynchrony was in the range of seconds, two orders of magnitude larger than previously reported for segregation of known odorants. This implies that segregation of unknown odorants may depend on other neural mechanisms such as sensory adaptation. Secondly, I asked what the behaviourally relevant timescales of temporal stimulus cues were for odour source segregation. I addressed this by presenting Drosophila melanogaster with pulses of binary mixtures of attractive and aversive odorants in a wind tunnel, examining their responses to different onset asynchrony times and odorant combinations. In my second chapter, I demonstrated that fruit flies can distinguish between synchronous and asynchronous mixtures of odorants of opposing valence, therefore could use this information for determining the number of odour sources in their environment. Thirdly, I asked whether social interactions between insects could affect their proficiency of foraging and their memory expression. I addressed this using an automated conditioning assay for Drosophila melanogaster, where I conditioned flies in different group sizes to associate an odorant with food, and tested their short-term memory for the conditioned odorant. In my third chapter, I demonstrated that the associative memory of the conditioned odorant is extended for flies conditioned and tested in larger groups, compared to flies conditioned and tested as individuals or in pairs. This extended memory expression could be due to the increased number of social interactions between flies in the larger group, through which flies could transfer information about the location and quality of the food source. Altogether, these three chapters provide evidence that insects can use temporal information to segregate relevant odorant stimuli from background mixtures and can use social information to improve source localisation.publishe
Segregation of unknown odors from mixtures based on stimulus onset asynchrony in honey bees
No full textAnimals use olfaction to search for distant objects. Unlike vision, where objects are spaced out, olfactory information mixes when it reaches olfactory organs. Therefore, efficient olfactory search requires segregating odors that are mixed with background odors. Animals can segregate known odors by detecting short differences in the arrival of mixed odorants (stimulus onset asynchrony). However, it is unclear whether animals can also use stimulus onset asynchrony to segregate odorants that they had no previous experience with and which have no innate or learned relevance (unknown odorants). Using behavioral experiments in honey bees, we here show that stimulus onset asynchrony also improves segregation of those unknown odorants. The stimulus onset asynchrony necessary to segregate unknown odorants is in the range of seconds, which is two orders of magnitude larger than the previously reported stimulus asynchrony sufficient for segregating known odorants. We propose that for unknown odorants, segregating odorant A from a mixture with B requires sensory adaptation to B.publishe
Data_Sheet_1_Segregation of Unknown Odors From Mixtures Based on Stimulus Onset Asynchrony in Honey Bees.CSV
No full textAnimals use olfaction to search for distant objects. Unlike vision, where objects are spaced out, olfactory information mixes when it reaches olfactory organs. Therefore, efficient olfactory search requires segregating odors that are mixed with background odors. Animals can segregate known odors by detecting short differences in the arrival of mixed odorants (stimulus onset asynchrony). However, it is unclear whether animals can also use stimulus onset asynchrony to segregate odorants that they had no previous experience with and which have no innate or learned relevance (unknown odorants). Using behavioral experiments in honey bees, we here show that stimulus onset asynchrony also improves segregation of those unknown odorants. The stimulus onset asynchrony necessary to segregate unknown odorants is in the range of seconds, which is two orders of magnitude larger than the previously reported stimulus asynchrony sufficient for segregating known odorants. We propose that for unknown odorants, segregating odorant A from a mixture with B requires sensory adaptation to B.</p
Alive-FP: Automated Verification of Floating Point Based Peephole Optimizations in LLVM
No full textPeephole optimizations optimize and canonicalize code to enable other optimizations but are error-prone. Our prior research on Alive, a domain-specific language for specifying LLVM’s peephole optimizations, automatically verifies the correctness of integer-based peephole optimizations and generates C++ code for use within LLVM. This paper proposes Alive-FP, an automated verification and code generation framework for floating point based peephole optimizations in LLVM. Alive-FP handles bit precise floating point optimizations and a class of fast-math optimizations involving signed zeros, not-a-number, and infinities. This paper provides multiple encodings for various floating point operations to account for the various kinds of undefined behavior and under-specification in the LLVM’s language reference manual. We have translated all optimizations that belong to this category into Alive-FP. In this process, we have discovered seven wrong optimizations in LLVM.Technical report DCS-TR-72
Olfactory Object Recognition Based on Fine-Scale Stimulus Timing in Drosophila
No full textSummary: Odorants of behaviorally relevant objects (e.g., food sources) intermingle with those from other sources. Therefore to determine whether an odor source is good or bad—without actually visiting it—animals first need to segregate the odorants from different sources. To do so, animals could use temporal stimulus cues, because odorants from one source exhibit correlated fluctuations, whereas odorants from different sources are less correlated. However, the behaviorally relevant timescales of temporal stimulus cues for odor source segregation remain unclear. Using behavioral experiments with free-flying flies, we show that (1) odorant onset asynchrony increases flies' attraction to a mixture of two odorants with opposing innate or learned valence and (2) attraction does not increase when the attractive odorant arrives first. These data suggest that flies can use stimulus onset asynchrony for odor source segregation and imply temporally precise neural mechanisms for encoding odors and for segregating them into distinct objects. : Biological Sciences; Entomology; Behavioral Neuroscience Subject Areas: Biological Sciences, Entomology, Behavioral Neuroscienc
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Generalized Pseudolikelihood Methods for Inverse Covariance Estimation
No full textCopyright 2017 by the author(s). We introduce PseudoNet, a new pseudolikelihood-based estimator of the inverse covariance matrix, that has a number of useful statistical and computational properties. We show, through detailed experiments with synthetic as well as real-world finance and wind power data, that PseudoNet outperforms related methods in terms of estimation error and support recovery, making it well-suited for use in a downstream application, where obtaining low estimation error can be important. We also show, under regularity conditions, that PseudoNet is consistent. Our proof assumes the existence of accurate estimates of the diagonal entries of the underlying inverse covariance matrix; we additionally provide a two-step method to obtain these estimates, even in a high-dimensional setting, going beyond the proofs for related methods. Unlike other pseudolikelihood-based methods, we also show that PseudoNet does not saturate, i.e., in high dimensions, there is no hard limit on the number of nonzero entries in the PseudoNet estimate. We present a fast algorithm as well as screening rules that make computing the PseudoNet estimate over a range of tuning parameters tractable
First principles investigations of Fe2CrSi Heusler alloys by substitution of Co at Fe site
No full textElectrical and electronics fundamentals course - Interactive simulations
No full textThis project is made possible with funding by the Government of Ontario and through eCampusOntario’s support of the Virtual Learning Strategy (VLS) and Ontario Exchange (OEX). To learn more about OEX visit: https://exchange.ecampusontario.ca/.Fanshawe College offers an Electrical and Electronics Fundamentals Course that provides learners with fundamental knowledge and skills in electrical circuit design and analysis, electronic component assembly and repair, electrical safety and hazard prevention, industrial automation and controls, electrical code, and regulation compliance.
The goal of this project is to enhance the learning experience of this existing course through the creation of engaging, high-quality, interactive simulations.
When using this OER, please include the following attribution statement and include the eCampus logo uploaded along with this resource.
Funded by the Government of Ontario.
The views expressed in this publication are the views of the author(s) and do not necessarily reflect those of the Government of Ontario or the Ontario Online Learning Consortium
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