Won’t Get Fooled Again: Why VW’s Emissions Deception Is Illegal in Europe and How to Improve the EU’s Auto Regulatory System

Replete with greed, hubris, and deceit, the Volkswagen emissions scandal is not your typical case of corporate wrongdoing. With a price tag of $20 million in the United States, it is already one of the most expensive corporate scandals in history and has caused significant damage to the environment, public health, and the global economy. Dieselgate has had a majorly disproportionate impact on Europe, where nearly nine million of the eleven million affected cars are located. The financial cost of the scandal, however, has been confined almost entirely to the United States, due to a European Union (EU) regulation that allows automakers to change their cars’ performance settings before emissions tests. This Note argues that this regulation, though considered a loophole by some, is not an escape hatch for European car manufacturers. Thus, it argues that Volkswagen’s use of defeat-device software violates EU law. With an eye toward preventing similar scandals in the future, it also recommends ways in which the EU can improve its auto regulatory system, and identifies the costs of allowing Volkswagen’s misconduct to go unpunished in Europe

\u3cem\u3eNovartis AG v. Union of India\u3c/em\u3e: Why the Court’s Narrow Interpretation of Enhanced Efficacy Threatens Domestic and Foreign Drug Development

Through the Patents (Amendment) Act of 2005, the Indian Parliament revised the Patents Act of 1970 to permit the grant of patents for pharmaceutical products. A core provision in the 2005 Amendment was Section 3(d), which prohibited granting patents to a new form of a known substance that did not enhance the efficacy of that substance. In Novartis AG v. Union of India, the Supreme Court of India applied this new provision to Novartis’s patent application for the final form of its drug Gleevec. The court engaged in an unreasonably narrow analysis of enhanced efficacy, potentially stifling secondary patents on important drugs and creating significant uncertainty for pharmaceutical companies going forward. Novartis AG evinces the ongoing tension between maintaining India’s status as the “pharmacy of the world” and promoting scientific innovation in South Asia

Seed Ecology and Regeneration Process to Inform Seed-Based Wetland Restoration

Wetlands provide immense value to wildlife and humans but have been degrading rapidly around the world. One major challenge is the loss of native plant species in wetlands, which limits the ability of wetlands to function as they should. Restoring wetlands requires a combination of removing the cause of degradation (such as invasive plant species) and, in many cases, actively returning native plants to the site especially via seeding. Further, early plant life stages are the most vulnerable for plants and is often the time in which sown species die and fail to establish. Thus, understanding how and why seeds die or survive across species and environmental conditions can provide guidance for seed-based wetland restoration. Here, we sought to answer these important knowledge gaps through a series of greenhouse and lab experiments. First, we sought to answer what native sowing rate was needed to maximize native plant performance across a gradient of invasive species seed density, environmental conditions, and timing of seed addition. Separately, we performed a lab and growth chamber experiment in which we measured important characteristics about seeds and seedlings (grown in different environmental conditions) to better understand (and ultimately predict) why some species do well and in what conditions that can occur. Finally, in a separate greenhouse experiment, we grew native and invasive wetland plants for eight-weeks and tracked whether seeds germinated, survived, or died in order to quantify plant transitions through these early life stages. We also assessed ‘end-of-season’ percent cover and the rate of clonal production to gauge how early stages of plant growth contributes to invasion resistance. We found native plant establishment increased with higher native sowing densities, especially when native seeds were sown early in the season. However, the biggest driver in plant community composition following seeding was the density of invasive Phragmites australis seeds in the soil. Low water levels yielded higher native plant performance and more effectively suppressed P. australis growth. We also identified characteristics of seeds and seedlings that explained their germination and early growth patterns—species that had light seeds with thin seed coats and shallow seed dormancy had faster time to germination and higher growth rates, while species with heavy seeds had thick seed coats, deep seed dormancy, slower germination, and higher resource allocation to plant structures. Finally, we found that high-water levels enhanced the probability of seed germination, and that high temperatures lead to higher clonal development in seedlings. Overall, Phragmites australis was a superior performer is early life stages, but Distichlis spicata performed well due to high germination probabilities and Eleocharis palustris performed well due to extensive clonal production. As seed-based wetland restoration becomes increasingly necessary, the findings from this dissertation provide guidance on which native species should be used, where seeds should be sourced, and what environmental conditions should be targeted to maximize native plant establishment and restore wetland functions

Round Goby, Neogobius Melanostomus, Abundance and Productivity in the Rocky Nearshore Zone of Lake Michigan

Few organisms are well adapted to efficiently feed on invasive dreissenid mussels, a dominant primary consumer in Lake Michigan and other lower Great Lakes. As a result, these mussels represent a potential trophic dead-end. However, round gobies (Neogobius melanostomus), an invasive species introduced to the Great Lakes region at the end of the 20th century, possess several adaptive advantages that allow them to make dreissenid mussels a significant portion of their diet. Since their invasion, round gobies have become the predominant shallow nearshore fish in Lake Michigan and their success, along with the success of dreissenid mussels, has caused major shifts in regional productivity, trophic structure, and energy flow pathways in the lake. In the Great lakes, round gobies have been incorporated into the diets of numerous piscivorous species, and therefore may serve as a conduit of energy, nutrients, and contaminants to higher trophic levels. This potential has made round gobies a critical species to consider in management plans, especially in regions important for Great Lakes fisheries. For management to be successful, a deeper understanding of round gobies\u27 effect on food web structure and energy flow is needed. This research aimed to quantify round goby abundance and productivity in the rocky nearshore zone of Lake Michigan, focusing on a rocky reef (10-11 m depth) in Good Harbor Bay near Sleeping Bear Dunes National Lakeshore (SLBE) that has historically been used for spawning and feeding by native fish species such as lake trout and lake whitefish. Productivity was estimated by quantifying several population and bioenergetic parameters in June-October 2020. Benthic sampling provided biomass estimates of nearshore primary producers and consumers as well as a stable isotope trophic baseline. Round goby population density and size-frequency were determined using visual and video transects. Age structure was estimated from sagittal otoliths and combined with length data to model growth. Round goby diet composition was determined based on gut content and stable isotope analysis and used to estimate the population\u27s reliance on benthic algae production vs. dreissenid grazing of phytoplankton. The combination of these methods allowed for an estimate of total round goby productivity on the rocky reef. A comparison of round goby productivity with energy inputs in the rocky nearshore zone allowed for trophic transfer efficiency to be estimated. Mean round goby density was 2.6 individuals ? m-2. The population was found to have a right-skewed unimodal size distribution with a mean size of 7.3 ± 2.4 cm (n = 1304) and a maximum size of 15.9 cm. Males from Good Harbor Reef have a faster growth rate and obtain a greater maximum size and age than females. Diet analysis indicated an ontogenetic diet shift, with larger gobies being more reliant on invasive mussels than smaller gobies. However, at the population level, non-mussel benthic invertebrates accounted for over half of round goby prey. Round goby productivity was estimated to be 0.009 g wet weight ? day-1 ? 0.041 kJ ? day-1. This resulted in an estimated reef transfer efficiency of 1.3 - 1.8% when accounting for both dreissenid and non-dreissenid benthic invertebrates. This low efficiency is due to only a small fraction of dreissenid production (3%) being consumed by round gobies. By contrast, round gobies appear to be consuming virtually all (81 - 122%) non-dreissenid benthic invertebrate productivity. On a lake-wide scale, annual round goby productivity was estimated to be four times that of recent estimates of alewife production. These results suggest that round gobies represent a substantial portion of Lake Michigan prey fish biomass and have the potential to serve as an important energetic conduit from the benthic region and invasive mussels to upper trophic levels

Partitioned Compressive Sensing with Neighbor-Weighted Decoding

Compressive sensing has gained momentum in recent years as an exciting new theory in signal processing with several useful applications. It states that signals known to have a sparse representation may be encoded and later reconstructed using a small number of measurements, approximately proportional to the signal s sparsity rather than its size. This paper addresses a critical problem that arises when scaling compressive sensing to signals of large length: that the time required for decoding becomes prohibitively long, and that decoding is not easily parallelized. We describe a method for partitioned compressive sensing, by which we divide a large signal into smaller blocks that may be decoded in parallel. However, since this process requires a signi cant increase in the number of measurements needed for exact signal reconstruction, we focus on mitigating artifacts that arise due to partitioning in approximately reconstructed signals. Given an error-prone partitioned decoding, we use large magnitude components that are detected with highest accuracy to in uence the decoding of neighboring blocks, and call this approach neighbor-weighted decoding. We show that, for applications with a prede ned error threshold, our method can be used in conjunction with partitioned compressive sensing to improve decoding speed, requiring fewer additional measurements than unweighted or locally-weighted decoding.Engineering and Applied Science

Hierarchical Sparse Coding for Wireless Link Prediction in an Airborne Scenario

We build a data-driven hierarchical inference model to predict wireless link quality between a mobile unmanned aerial vehicle (UAV) and ground nodes. Clustering, sparse feature extraction, and non-linear pooling are combined to improve Support Vector Machine (SVM) classification when a limited training set does not comprehensively characterize data variations. Our approach first learns two layers of dictionaries by clustering packet reception data. These dictionaries are used to perform sparse feature extraction, which expresses link state vectors first in terms of a few prominent local patterns, or features, and then in terms of co-occurring features along the flight path. In order to tolerate artifacts like small positional shifts in field-collected data, we pool large magnitude features among overlapping shifted patches within windows. Together, these techniques transform raw link measurements into stable feature vectors that capture environmental effects driven by radio range limitations, antenna pattern variations, line-of-sight occlusions, etc. Link outage prediction is implemented by an SVM that assigns a common label to feature vectors immediately preceding gaps of successive packet losses, predictions are then fed to an adaptive link layer protocol that adjusts forward error correction rates, or queues packets during outages to prevent TCP timeout. In our harsh target environment, links are unstable and temporary outages common, so baseline TCP connections achieve only minimal throughput. However, connections under our predictive protocol temporarily hold packets that would otherwise be lost on unavailable links, and react quickly when the UAV link is restored, increasing overall channel utilization.Engineering and Applied Science

Workload Prediction for Adaptive Power Scaling Using Deep Learning

We apply hierarchical sparse coding, a form of deep learning, to model user-driven workloads based on on-chip hardware performance counters. We then predict periods of low instruction throughput, during which frequency and voltage can be scaled to reclaim power. Using a multi-layer coding structure, our method progressively codes counter values in terms of a few prominent features learned from data, and passes them to a Support Vector Machine (SVM) classifier where they act as signatures for predicting future workload states. We show that prediction accuracy and look-ahead range improve significantly over linear regression modeling, giving more time to adjust power management settings. Our method relies on learning and feature extraction algorithms that can discover and exploit hidden statistical invariances specific to workloads. We argue that, in addition to achieving superior prediction performance, our method is fast enough for practical use. To our knowledge, we are the first to use deep learning at the instruction level for workload prediction and on-chip power adaptation.Engineering and Applied Science