RoadRunner: Infrastructure-less vehicular congestion control

RoadRunner is an in-vehicle app for traffic congestion control without costly roadside infrastructure, instead judiciously harnessing vehicle-to-vehicle communications, cellular connectivity, and onboard computation and sensing to enable large-scale traffic congestion control at higher penetration and finer granularity than previously possible. RoadRunner limits the number of vehicles in a congested region or road by requiring each to possess a token for entry. Tokens can circulate and be reused among multiple vehicles as vehicles move between regions. We built RoadRunner as an Android app utilizing LTE, 802.11p, and 802.11n radios, deployed it on 10 vehicles, and measured cellular access reductions of up to 84% and response time improvements of up to 80%. In a microscopic agent-based traffic simulator, RoadRunner achieved travel speed improvements of up to 7.7% over an industry-strength electronic road pricing system.Singapore-MIT Alliance for Research and TechnologyAmerican Society for Engineering Education. National Defense Science and Engineering Graduate Fellowshi

Distributed mobile platforms and applications for intelligent transportation systems

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (p. 70-75).Smartphones are pervasive, and possess powerful processors, multi-faceted sensing, and multiple radios. However, networked mobile apps still typically use a client-server programming model, sending all shared data queries and uploads through the cellular network, incurring bandwidth consumption and unpredictable latencies. Leveraging the local compute power and device-to-device communications of modern smartphones can mitigate demand on cellular networks and improve response times. This thesis presents two systems towards this vision. First, we present DIPLOMA, which aids developers in achieving this vision by providing a programming layer to easily program a collection of smartphones connected over adhoc wireless. It presents a familiar shared data model to developers, while underneath, it implements a distributed shared memory system that provides coherent relaxed-consistency access to data across different smartphones and addresses the issues that device mobility and unreliable networking pose against consistency and coherence. We evaluated our prototype on 10 Android phones on both 3G (HSPA) and 4G (LTE) networks with a representative location-based photo-sharing service and a synthetic benchmark. We also simulated large scale scenarios up to 160 nodes on the ns-2 network simulator. Compared to a client-server baseline, our system shows response time improvements of 10x over 3G and 2x over 4G. We also observe cellular bandwidth reductions of 96%, comparable energy consumption, and a 95.3% request completion rate with coherent caching. With RoadRunner, we apply our vision to Intelligent Transportation Systems (ITS). RoadRunner implements vehicular congestion control as an in-vehicle smartphone app that judiciously harnesses onboard sensing, local computation, and short-range communications, enabling large-scale traffic congestion control without the need for physical infrastructure, at higher penetration across road networks, and at finer granularity. RoadRunner enforces a quota on the number of cars on a road by requiring vehicles to possess a token for entry. Tokens are circulated and reused among multiple vehicles as they move between regions. We implemented RoadRunner as an Android application, deployed it on 10 vehicles using 4G (LTE), 802.11p DSRC and 802.11n adhoc WiFi, and measured cellular access reductions up to 84%, response time improvements up to 80%, and effectiveness of the system in enforcing congestion control policies. We also simulated large-scale scenarios using actual traffic loop-detector counts from Singapore.by Jason Hao Gao.S.M

A Case for Leveraging 802.11p for Direct Phone-to-Phone Communications

WiFi cannot effectively handle the demands of device-to-device communication between phones, due to insufficient range and poor reliability. We make the case for using IEEE 802.11p DSRC instead, which has been adopted for vehicle-to-vehicle communications, providing lower latency and longer range. We demonstrate a prototype motivated by a novel fabrication process that deposits both III-V and CMOS devices on the same die. In our system prototype, the designed RF front-end is interfaced with a baseband processor on an FPGA, connected to Android phones. It consumes 0.02uJ/bit across 100m assuming free space. Application-level power control dramatically reduces power consumption by 47-56%.Singapore-MIT Alliance for Research and TechnologyAmerican Society for Engineering Education. National Defense Science and Engineering Graduate Fellowshi

Contact sensing and grasping performance of compliant hands

Limitations in modern sensing technologies result in large errors in sensed target object geometry and location in unstructured environments. As a result, positioning a robotic end-effector includes inherent error that will often lead to unsuccessful grasps. In previous work, we demonstrated that optimized configuration, compliance, viscosity, and adaptability in the mechanical structure of a robot hand facilitates reliable grasping in unstructured environments, even with purely feedforward control of the hand. In this paper we describe the addition of a simple contact sensor to the fingerpads of the SDM Hand (Shape Deposition Manufactured Hand), which, along with a basic control algorithm, significantly expands the grasp space of the hand and reduces contact forces during the acquisition phase of the grasp. The combination of the passive mechanics of the SDM Hand along with this basic sensor suite enables positioning errors of over 5 cm in any direction. In the context of mobile manipulation, the performance demonstrated here may reduce the need for much of the complex array of sensing currently utilized on mobile platforms, greatly increase reliability, and speed task execution, which can often be prohibitively slow.Engineering and Applied Science

Local Benchmarks for the Evolution of Major-Merger Galaxies -- Spitzer Observations of a K-Band Selected Sample

We present Spitzer observations for a sample of close major-merger galaxy pairs (KPAIR sample) selected from 2MASS/SDSS-DR3 cross-matches. The goals are to study the star formation activity in these galaxies and to set a local bench mark for the cosmic evolution of close major mergers. The Spitzer KPAIR sample (27 pairs, 54 galaxies) includes all spectroscopically confirmed S+S and S+E pairs in a parent sample that is complete for primaries brighter than K=12.5 mag, projected separations of 5< s < 20 kpc/h, and mass ratios<2.5. The Spitzer data consist of images in 7 bands (3.6, 4.5, 5.8, 8, 24, 70, 160 um). Compared to single spiral galaxies in a control sample, only spiral galaxies in S+S pairs show significantly enhanced specific star formation rate (sSFR=SFR/M), whereas spiral galaxies in S+E pairs do not. Furthermore, the SFR enhancement of spiral galaxies in S+S pairs is highly mass-dependent. Only those with \rm M \gsim 10^{10.5} M_\sun show significant enhancement. Relatively low mass (\rm M \sim 10^{10} M_\sun) spirals in S+S pairs have about the same SFR/M compared to their counterparts in the control sample. There is evidence for a correlation between the global star formation activities (but not the nuclear activities) of the component galaxies in massive S+S major-merger pairs (the "Holmberg effect"). There is no significant difference in the SFR/M between the primaries and the secondaries, nor between spirals of SEP<1 and those of SEP.1. The contribution of KPAIR galaxies to the cosmic SFR density in the local universe is only 1.7%.Comment: 73 pages; accpected by Ap

Host-Based Th2 Cell Therapy for Prolongation of Cardiac Allograft Viability

Donor T cell transfusion, which is a long-standing approach to prevent allograft rejection, operates indirectly by alteration of host T cell immunity. We therefore hypothesized that adoptive transfer of immune regulatory host Th2 cells would represent a novel intervention to enhance cardiac allograft survival. Using a well-described rat cardiac transplant model, we first developed a method for ex vivo manufacture of rat host-type Th2 cells in rapamycin, with subsequent injection of such Th2.R cells prior to class I and class II disparate cardiac allografting. Second, we determined whether Th2.R cell transfer polarized host immunity towards a Th2 phenotype. And third, we evaluated whether Th2.R cell therapy prolonged allograft viability when used alone or in combination with a short-course of cyclosporine (CSA) therapy. We found that host-type Th2.R cell therapy prior to cardiac allografting: (1) reduced the frequency of activated T cells in secondary lymphoid organs; (2) shifted post-transplant cytokines towards a Th2 phenotype; and (3) prolonged allograft viability when used in combination with short-course CSA therapy. These results provide further support for the rationale to use “direct” host T cell therapy for prolongation of allograft viability as an alternative to “indirect” therapy mediated by donor T cell infusion

The Genomes of Oryza sativa: A History of Duplications

We report improved whole-genome shotgun sequences for the genomes of indica and japonica rice, both with multimegabase contiguity, or almost 1,000-fold improvement over the drafts of 2002. Tested against a nonredundant collection of 19,079 full-length cDNAs, 97.7% of the genes are aligned, without fragmentation, to the mapped super-scaffolds of one or the other genome. We introduce a gene identification procedure for plants that does not rely on similarity to known genes to remove erroneous predictions resulting from transposable elements. Using the available EST data to adjust for residual errors in the predictions, the estimated gene count is at least 38,000–40,000. Only 2%–3% of the genes are unique to any one subspecies, comparable to the amount of sequence that might still be missing. Despite this lack of variation in gene content, there is enormous variation in the intergenic regions. At least a quarter of the two sequences could not be aligned, and where they could be aligned, single nucleotide polymorphism (SNP) rates varied from as little as 3.0 SNP/kb in the coding regions to 27.6 SNP/kb in the transposable elements. A more inclusive new approach for analyzing duplication history is introduced here. It reveals an ancient whole-genome duplication, a recent segmental duplication on Chromosomes 11 and 12, and massive ongoing individual gene duplications. We find 18 distinct pairs of duplicated segments that cover 65.7% of the genome; 17 of these pairs date back to a common time before the divergence of the grasses. More important, ongoing individual gene duplications provide a never-ending source of raw material for gene genesis and are major contributors to the differences between members of the grass family

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages