Reducing Reparameterization Gradient Variance

Optimization with noisy gradients has become ubiquitous in statistics and machine learning. Reparameterization gradients, or gradient estimates computed via the "reparameterization trick," represent a class of noisy gradients often used in Monte Carlo variational inference (MCVI). However, when these gradient estimators are too noisy, the optimization procedure can be slow or fail to converge. One way to reduce noise is to use more samples for the gradient estimate, but this can be computationally expensive. Instead, we view the noisy gradient as a random variable, and form an inexpensive approximation of the generating procedure for the gradient sample. This approximation has high correlation with the noisy gradient by construction, making it a useful control variate for variance reduction. We demonstrate our approach on non-conjugate multi-level hierarchical models and a Bayesian neural net where we observed gradient variance reductions of multiple orders of magnitude (20-2,000x)

Lane Discovery in Traffic Video

Video sensing has become very important in Intelligent Transportation Systems (ITS) due to its relative low cost and non-invasive deployment. An effective ITS requires detailed traffic information, including vehicle volume counts for each lane in surveillance video of a highway or an intersection. The multiple-target, vehicle-tracking and counting problem is most reliably solved in a reduced space defined by the constraints of the vehicles driving within lanes. This requires lanes to be pre-specified. An off-line pre-processing method is presented which automatically discovers traffic lanes from vehicle motion in uncalibrated video from a stationary camera. A moving vehicle density map is constructed, then multiple lane curves are fitted. Traffic lanes are found without relying on possibly noisy tracked vehicle trajectories

Ekiden: A Platform for Confidentiality-Preserving, Trustworthy, and Performant Smart Contract Execution

Smart contracts are applications that execute on blockchains. Today they manage billions of dollars in value and motivate visionary plans for pervasive blockchain deployment. While smart contracts inherit the availability and other security assurances of blockchains, however, they are impeded by blockchains' lack of confidentiality and poor performance. We present Ekiden, a system that addresses these critical gaps by combining blockchains with Trusted Execution Environments (TEEs). Ekiden leverages a novel architecture that separates consensus from execution, enabling efficient TEE-backed confidentiality-preserving smart-contracts and high scalability. Our prototype (with Tendermint as the consensus layer) achieves example performance of 600x more throughput and 400x less latency at 1000x less cost than the Ethereum mainnet. Another contribution of this paper is that we systematically identify and treat the pitfalls arising from harmonizing TEEs and blockchains. Treated separately, both TEEs and blockchains provide powerful guarantees, but hybridized, though, they engender new attacks. For example, in naive designs, privacy in TEE-backed contracts can be jeopardized by forgery of blocks, a seemingly unrelated attack vector. We believe the insights learned from Ekiden will prove to be of broad importance in hybridized TEE-blockchain systems

Effect of spirometry on intra-thoracic pressures

Due to the high intra-thoracic pressures associated with forced vital capacity manoeuvres, spirometry is contraindicated for vulnerable patients. However, the typical pressure response to spirometry has not been reported. Eight healthy, recreationally-active men performed spirometry while oesophageal pressure was recorded using a latex balloon-tipped catheter. Peak oesophageal pressure during inspiration was - 47 ± 9 cmH O (37 ± 10% of maximal inspiratory pressure), while peak oesophageal pressure during forced expiration was 102 ± 34 cmH O (75 ± 17% of maximal expiratory pressure). The deleterious consequences of spirometry might be associated with intra-thoracic pressures that approach maximal values during forced expiration

Impaired β-glucocerebrosidase activity and processing in frontotemporal dementia due to progranulin mutations.

Loss-of-function mutations in progranulin (GRN) are a major autosomal dominant cause of frontotemporal dementia. Most pathogenic GRN mutations result in progranulin haploinsufficiency, which is thought to cause frontotemporal dementia in GRN mutation carriers. Progranulin haploinsufficiency may drive frontotemporal dementia pathogenesis by disrupting lysosomal function, as patients with GRN mutations on both alleles develop the lysosomal storage disorder neuronal ceroid lipofuscinosis, and frontotemporal dementia patients with GRN mutations (FTD-GRN) also accumulate lipofuscin. The specific lysosomal deficits caused by progranulin insufficiency remain unclear, but emerging data indicate that progranulin insufficiency may impair lysosomal sphingolipid-metabolizing enzymes. We investigated the effects of progranulin insufficiency on sphingolipid-metabolizing enzymes in the inferior frontal gyrus of FTD-GRN patients using fluorogenic activity assays, biochemical profiling of enzyme levels and posttranslational modifications, and quantitative neuropathology. Of the enzymes studied, only β-glucocerebrosidase exhibited impairment in FTD-GRN patients. Brains from FTD-GRN patients had lower activity than controls, which was associated with lower levels of mature β-glucocerebrosidase protein and accumulation of insoluble, incompletely glycosylated β-glucocerebrosidase. Immunostaining revealed loss of neuronal β-glucocerebrosidase in FTD-GRN patients. To investigate the effects of progranulin insufficiency on β-glucocerebrosidase outside of the context of neurodegeneration, we investigated β-glucocerebrosidase activity in progranulin-insufficient mice. Brains from Grn-/- mice had lower β-glucocerebrosidase activity than wild-type littermates, which was corrected by AAV-progranulin gene therapy. These data show that progranulin insufficiency impairs β-glucocerebrosidase activity in the brain. This effect is strongest in neurons and may be caused by impaired β-glucocerebrosidase processing