Dynamic Arrival Rate Estimation for Campus Mobility on Demand Network Graphs

Mobility On Demand (MOD) systems are revolutionizing transportation in urban settings by improving vehicle utilization and reducing parking congestion. A key factor in the success of an MOD system is the ability to measure and respond to real-time customer arrival data. Real time traffic arrival rate data is traditionally difficult to obtain due to the need to install fixed sensors throughout the MOD network. This paper presents a framework for measuring pedestrian traffic arrival rates using sensors onboard the vehicles that make up the MOD fleet. A novel distributed fusion algorithm is presented which combines onboard LIDAR and camera sensor measurements to detect trajectories of pedestrians with a 90% detection hit rate with 1.5 false positives per minute. A novel moving observer method is introduced to estimate pedestrian arrival rates from pedestrian trajectories collected from mobile sensors. The moving observer method is evaluated in both simulation and hardware and is shown to achieve arrival rate estimates comparable to those that would be obtained with multiple stationary sensors.Comment: Appears in 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). http://ieeexplore.ieee.org/abstract/document/7759357

Predictive Modeling of Pedestrian Motion Patterns with Bayesian Nonparametrics

For safe navigation in dynamic environments, an autonomous vehicle must be able to identify and predict the future behaviors of other mobile agents. A promising data-driven approach is to learn motion patterns from previous observations using Gaussian process (GP) regression, which are then used for online prediction. GP mixture models have been subsequently proposed for finding the number of motion patterns using GP likelihood as a similarity metric. However, this paper shows that using GP likelihood as a similarity metric can lead to non-intuitive clustering configurations - such as grouping trajectories with a small planar shift with respect to each other into different clusters - and thus produce poor prediction results. In this paper we develop a novel modeling framework, Dirichlet process active region (DPAR), that addresses the deficiencies of the previous GP-based approaches. In particular, with a discretized representation of the environment, we can explicitly account for planar shifts via a max pooling step, and reduce the computational complexity of the statistical inference procedure compared with the GP-based approaches. The proposed algorithm was applied on two real pedestrian trajectory datasets collected using a 3D Velodyne Lidar, and showed 15% improvement in prediction accuracy and 4.2 times reduction in computational time compared with a GP-based algorithm.Ford Motor Compan

Triptolide Transcriptionally Represses HER2 in Ovarian Cancer Cells by Targeting NF- κ

Triptolide (TPL) inhibits the proliferation of a variety of cancer cells and has been proposed as an effective anticancer agent. In this study, we demonstrate that TPL downregulates HER2 protein expression in oral, ovarian, and breast cancer cells. It suppresses HER2 protein expression in a dose- and time-dependent manner. Transrepression of HER2 promoter activity by TPL is also observed. The interacting site of TPL on the HER2 promoter region is located between −207 and −103 bps, which includes a putative binding site for the transcription factor NF-κB. Previous reports demonstrated that TPL suppresses NF-κB expression. We demonstrate that overexpression of NF-κB rescues TPL-mediated suppression of HER2 promoter activity and protein expression in NIH3T3 cells and ovarian cancer cells, respectively. In addition, TPL downregulates the activated (phosphorylated) forms of HER2, phosphoinositide-3 kinase (PI3K), and serine/threonine-specific protein kinase (Akt). TPL also inhibits tumor growth in a mouse model. Furthermore, TPL suppresses HER2 and Ki-67 expression in xenografted tumors based on an immunohistochemistry (IHC) assay. These findings suggest that TPL transrepresses HER2 and suppresses the downstream PI3K/Akt-signaling pathway. Our study reveals that TPL can inhibit tumor growth and thereby may serve as a potential chemotherapeutic agent

Pressure Dependence of Fragile-to-Strong Transition and a Possible Second Critical Point in Supercooled Confined Water

By confining water in nano-pores of silica glass, we can bypass the crystallization and study the pressure effect on the dynamical behavior in deeply supercooled state using neutron scattering. We observe a clear evidence of a cusp-like fragile-to-strong (F-S) dynamic transition. Here we show that the transition temperature decreases steadily with an increasing pressure, until it intersects the homogenous nucleation temperature line of bulk water at a pressure of 1600 bar. Above this pressure, it is no longer possible to discern the characteristic feature of the F-S transition. Identification of this end point with the possible second critical point is discussed.Comment: 4 pages, 3 figure

Nano-to-micro self-assembly using shear flow devices

This work aims at developing a new technique to precisely assemble nano-materials into micro-or even meso-scale devices. For example, our long-term goal is to use massively architected motor-molecules to build muscle-like actuators in which these molecules work in parallel to output large forces. As an important first step, we report here the successful development of a much improved shear-flow-enhanced self-assembly method over the baseline spontaneous assembly method in test tubes. More specifically, we have engineered special thiolated model molecules (bisdisulfide/C_(28)H_(34)O_4S_4) and demonstrated the nano-to-micro self-assembly through flow interface using thiol-gold bonding chemistry. Our method has produced gold/molecule aggregates as big as 50 µm that are completely made of 30 nm gold nanoparticles and 3 nm model molecules

Semantic-level decentralized multi-robot decision-making using probabilistic macro-observations

Robust environment perception is essential for decision-making on robots operating in complex domains. Intelligent task execution requires principled treatment of uncertainty sources in a robot's observation model. This is important not only for low-level observations (e.g., accelerom-eter data), but also for high-level observations such as semantic object labels. This paper formalizes the concept of macro-observations in Decentralized Partially Observable Semi-Markov Decision Processes (Dec-POSMDPs), allowing scalable semantic-level multi-robot decision making. A hierarchical Bayesian approach is used to model noise statistics of low-level classifier outputs, while simultaneously allowing sharing of domain noise characteristics between classes. Classification accuracy of the proposed macro-observation scheme, called Hierarchical Bayesian Noise Inference (HBNI), is shown to exceed existing methods. The macro-observation scheme is then integrated into a Dec-POSMDP planner, with hardware experiments running onboard a team of dynamic quadrotors in a challenging domain where noise-agnostic filtering fails. To the best of our knowledge, this is the first demonstration of a real-time, convolutional neural net-based classification framework running fully onboard a team of quadrotors in a multi-robot decision-making domain.Boeing Compan