Algorithmic Notification and Monetization: Using Youtube’s Content ID System as a Model for European Union Copyright Reform

Article published in the Michigan State International Law Review

DeepPicar: A Low-cost Deep Neural Network-based Autonomous Car

We present DeepPicar, a low-cost deep neural network based autonomous car platform. DeepPicar is a small scale replication of a real self-driving car called DAVE-2 by NVIDIA. DAVE-2 uses a deep convolutional neural network (CNN), which takes images from a front-facing camera as input and produces car steering angles as output. DeepPicar uses the same network architecture---9 layers, 27 million connections and 250K parameters---and can drive itself in real-time using a web camera and a Raspberry Pi 3 quad-core platform. Using DeepPicar, we analyze the Pi 3's computing capabilities to support end-to-end deep learning based real-time control of autonomous vehicles. We also systematically compare other contemporary embedded computing platforms using the DeepPicar's CNN-based real-time control workload. We find that all tested platforms, including the Pi 3, are capable of supporting the CNN-based real-time control, from 20 Hz up to 100 Hz, depending on hardware platform. However, we find that shared resource contention remains an important issue that must be considered in applying CNN models on shared memory based embedded computing platforms; we observe up to 11.6X execution time increase in the CNN based control loop due to shared resource contention. To protect the CNN workload, we also evaluate state-of-the-art cache partitioning and memory bandwidth throttling techniques on the Pi 3. We find that cache partitioning is ineffective, while memory bandwidth throttling is an effective solution.Comment: To be published as a conference paper at RTCSA 201

Analysis and Mitigation of Shared Resource Contention on Heterogeneous Multicore: An Industrial Case Study

In this paper, we address the industrial challenge put forth by ARM in ECRTS 2022. We systematically analyze the effect of shared resource contention to an augmented reality head-up display (AR-HUD) case-study application of the industrial challenge on a heterogeneous multicore platform, NVIDIA Jetson Nano. We configure the AR-HUD application such that it can process incoming image frames in real-time at 20Hz on the platform. We use micro-architectural denial-of-service (DoS) attacks as aggressor tasks of the challenge and show that they can dramatically impact the latency and accuracy of the AR-HUD application, which results in significant deviations of the estimated trajectories from the ground truth, despite our best effort to mitigate their influence by using cache partitioning and real-time scheduling of the AR-HUD application. We show that dynamic LLC (or DRAM depending on the aggressor) bandwidth throttling of the aggressor tasks is an effective mean to ensure real-time performance of the AR-HUD application without resorting to over-provisioning the system

Multi-level Feature Fusion-based CNN for Local Climate Zone Classification from Sentinel-2 Images: Benchmark Results on the So2Sat LCZ42 Dataset

As a unique classification scheme for urban forms and functions, the local climate zone (LCZ) system provides essential general information for any studies related to urban environments, especially on a large scale. Remote sensing data-based classification approaches are the key to large-scale mapping and monitoring of LCZs. The potential of deep learning-based approaches is not yet fully explored, even though advanced convolutional neural networks (CNNs) continue to push the frontiers for various computer vision tasks. One reason is that published studies are based on different datasets, usually at a regional scale, which makes it impossible to fairly and consistently compare the potential of different CNNs for real-world scenarios. This study is based on the big So2Sat LCZ42 benchmark dataset dedicated to LCZ classification. Using this dataset, we studied a range of CNNs of varying sizes. In addition, we proposed a CNN to classify LCZs from Sentinel-2 images, Sen2LCZ-Net. Using this base network, we propose fusing multi-level features using the extended Sen2LCZ-Net-MF. With this proposed simple network architecture and the highly competitive benchmark dataset, we obtain results that are better than those obtained by the state-of-the-art CNNs, while requiring less computation with fewer layers and parameters. Large-scale LCZ classification examples of completely unseen areas are presented, demonstrating the potential of our proposed Sen2LCZ-Net-MF as well as the So2Sat LCZ42 dataset. We also intensively investigated the influence of network depth and width and the effectiveness of the design choices made for Sen2LCZ-Net-MF. Our work will provide important baselines for future CNN-based algorithm developments for both LCZ classification and other urban land cover land use classification

Eliciting Substance from ‘Hot Air': Financial Market Responses to EU Summit Decisions on European Defense

The results of deliberations in multilateral fora are often considered ineffective. Decision making in the European Union (EU) and in particular its key intergovernmental body, the European Council, poses no exception. Especially in the domain of EU foreign and security affairs, the unanimity requirement governing this institution allegedly allows nationalist governments to torpedo any attempt to build up a credible European defense force and a unified foreign policy stance. In this article, we take issue with the claim that multilateral summits merely result in "hot air” by looking at whether and how decisions made during EU summit meetings affect the European defense industry. We argue that investors react positively to a successful strengthening of Europe's military component—a vital part of the intensified cooperation within the European Security and Defense Policy (ESDP)—since such decisions increase the demand for military products and raise the expected profits in the European defense industry. Our findings lend empirical support to the view that financial markets indeed evaluate the substance of European Council meetings and react positively to those summit decisions that consolidate EU military capabilities and the ESDP. Each of the substantial council decisions studied increased the value of the European defense sector by about 4 billion euros on average. This shows that multilateral decisions can have considerable economic and financial repercussion

Assessment of the effect of esterified propoxylated glycerol (EPG) on the status of fat-soluble vitamins and select water-soluble nutrients following dietary administration to humans for 8weeks

AbstractThis double-blind, randomized, controlled study assessed the effect of esterified propoxylated glycerol (EPG) on fat-soluble vitamins and select nutrients in human subjects. For 8weeks, 139 healthy volunteers consumed a core diet providing adequate caloric and nutrient intakes. The diet included items (spread, muffins, cookies, and biscuits) providing EPG (10, 25, and 40g/day) vs. margarine alone (control). EPG did not significantly affect circulating retinol, α-tocopherol, or 25-OH D2, but circulating β-carotene and phylloquinone were lower in the EPG groups, and PIVKA-II levels were higher; 25-OH D3 increased but to a lesser extent than the control. The effect might be related to EPG acting as a lipid “sink” during gastrointestinal transit. No effects were seen in secondary endpoint measures (physical exam, clinical pathology, serum folate, RBC folate, vitamin B12, zinc, iron, calcium, phosphorus, osteocalcin, RBP, intact PTH, PT, PTT, cholesterol, HDL-C, LDL-C, triglycerides). Gastrointestinal adverse events (gas with discharge; diarrhea; oily spotting; oily evacuation; oily stool; liquid stool; soft stool) were reported more frequently by subjects receiving 25 or 40g/day of EPG. In general, the incidence and duration of these symptoms correlated directly with EPG dietary concentration. The results suggest 10g/day of EPG was reasonably well tolerated

Compulsory voting, habit formation, and political participation

Can electoral institutions induce lasting changes in citizens’ voting habits? We study the long-term and spillover effects of compulsory voting in the Swiss canton of Vaud (1900–1970) and find that this intervention increases turnout in federal referendums by 30 percentage points. However, despite its magnitude, the effect disappears quickly after voting is no longer compulsory. We find minor spillover effects on related forms of political participation that also vanish immediately after compulsory voting has been abolished. Overall, these results question habit formation arguments in the context of compulsory voting

Algorithmic Notification and Monetization: Using Youtube’s Content ID System as a Model for European Union Copyright Reform

Article published in the Michigan State International Law Review