Distributed traffic signal control using fuzzy logic

We present a distributed approach to traffic signal control, where the signal timing parameters at a given intersection are adjusted as functions of the local traffic condition and of the signal timing parameters at adjacent intersections. Thus, the signal timing parameters evolve dynamically using only local information to improve traffic flow. This distributed approach provides for a fault-tolerant, highly responsive traffic management system. The signal timing at an intersection is defined by three parameters: cycle time, phase split, and offset. We use fuzzy decision rules to adjust these three parameters based only on local information. The amount of change in the timing parameters during each cycle is limited to a small fraction of the current parameters to ensure smooth transition. We show the effectiveness of this method through simulation of the traffic flow in a network of controlled intersections

When Does Competition Lead to Efficient Investments?

The paper studies agents’ investment decisions between general and speci…c in-vestments under di¤erent ownership structures in a thin, decentralized market where each agent’s decision a¤ects the decisions and welfare of other (otherwise unrelated) agents mainly through indirect market linkages. The paper demonstrates that “excess competition among investors,” in every equilibrium, will lead to e¢cient investments, regardless of asset ownership. In the absence of such excess competition, in every equilibrium, ine¢cient investments will result, unless some special ownership arrange-ment is made. The problem in which the choice variable is investment level, instead of investment type, is also studied.

Does the BICEP2 Observation of Cosmological Tensor Modes Imply an Era of Nearly Planckian Energy Densities?

BICEP2 observations, interpreted most simply, suggest an era of inflation with energy densities of order ($10^{16}\, {\rm GeV})^4$, not far below the Planck density. However, models of TeV gravity with large extra dimensions might allow a very different interpretation involving much more modest energy scales. We discuss the viability of inflation in such models, and conclude that existing scenarios do not provide attractive alternatives to single field inflation in four dimensions. Because the detection of tensor modes strengthens our confidence that inflation occurred, it disfavors models of large extra dimensions, at least for the moment.Comment: 4 pages, v3: version to appear in JHE

Determination of Nonlinear Genetic Architecture using Compressed Sensing

We introduce a statistical method that can reconstruct nonlinear genetic models (i.e., including epistasis, or gene-gene interactions) from phenotype-genotype (GWAS) data. The computational and data resource requirements are similar to those necessary for reconstruction of linear genetic models (or identification of gene-trait associations), assuming a condition of generalized sparsity, which limits the total number of gene-gene interactions. An example of a sparse nonlinear model is one in which a typical locus interacts with several or even many others, but only a small subset of all possible interactions exist. It seems plausible that most genetic architectures fall in this category. Our method uses a generalization of compressed sensing (L1-penalized regression) applied to nonlinear functions of the sensing matrix. We give theoretical arguments suggesting that the method is nearly optimal in performance, and demonstrate its effectiveness on broad classes of nonlinear genetic models using both real and simulated human genomes.Comment: 20 pages, 8 figures. arXiv admin note: text overlap with arXiv:1408.342

Instability of Quantum de Sitter Spacetime

Quantized fields (e.g., the graviton itself) in de Sitter (dS) spacetime lead to particle production: specifically, we consider a thermal spectrum resulting from the dS (horizon) temperature. The energy required to excite these particles reduces slightly the rate of expansion and eventually modifies the semiclassical spacetime geometry. The resulting manifold no longer has constant curvature nor time reversal invariance, and back-reaction renders the classical dS background unstable to perturbations. In the case of AdS, there exists a global static vacuum state; in this state there is no particle production and the analogous instability does not arise.Comment: 3 pages, v2: version to appear in JHE

Chemokine responses in schistosomal antigen-elicited granuloma formation *

Host immune systems have evolved specialized responses to multicellular parasites. This is well represented by the type 2 granulomatous response to Schistosoma mansoni egg antigens, which is an eosinophil-rich inflammatory response mediated by Th2-associated cytokines. Using Ag-bead models of pulmonary granuloma formation in mice, we defined characteristic chemokine (CK) profiles in the granulomatous lungs. Our findings point to a role for C-C chemokine receptor-2 (CCR2) and CCR3 agonists such as monocyte chemotactic proteins (MCPs) 1/CCL2, 3/CCL7 and 5/CCL12 as important participants that are subject to regulation by Th2 cytokines interleukin (IL)-4 and IL-13. CCR4 and CCR8 agonists are also likely contributors. Analysis of CK receptor knockout mice revealed that CCR2 ligands (e.g. MCP-1 and 5) promoted early phase granuloma macrophage accumulation, whereas anti-MCP-3 (CCL7) antibody treatment abrogated eosinophil recruitment. CCR8 knockout mice also demonstrated impaired eosinophil recruitment but this appeared to be related to impaired Th2 cell function. Transcript analysis of CD4 + T cells generated during schistosome granuloma formation failed to show biased CCR8 expression but, having a more limited receptor repertoire, these cells were likely more dependent on CCR8 ligands. Together, these studies indicate an intricate involvement of chemokines in various stages and aspects of schistosomal egg Ag-elicited granuloma formation.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/74858/1/j.1365-3024.2002.00466.x.pd

Selective Communication for Cooperative Perception in End-to-End Autonomous Driving

The reliability of current autonomous driving systems is often jeopardized in situations when the vehicle's field-of-view is limited by nearby occluding objects. To mitigate this problem, vehicle-to-vehicle communication to share sensor information among multiple autonomous driving vehicles has been proposed. However, to enable timely processing and use of shared sensor data, it is necessary to constrain communication bandwidth, and prior work has done so by restricting the number of other cooperative vehicles and randomly selecting the subset of vehicles to exchange information with from all those that are within communication range. Although simple and cost effective from a communication perspective, this selection approach suffers from its susceptibility to missing those vehicles that possess the perception information most critical to navigation planning. Inspired by recent multi-agent path finding research, we propose a novel selective communication algorithm for cooperative perception to address this shortcoming. Implemented with a lightweight perception network and a previously developed control network, our algorithm is shown to produce higher success rates than a random selection approach on previously studied safety-critical driving scenario simulations, with minimal additional communication overhead.Comment: Scalable Autonomous Driving Workshop of IEEE International Conference on Robotics and Automation (ICRA Workshop), 202