Optimal Alarms for Vehicular Collision Detection

An important application of intelligent vehicles is advance detection of dangerous events such as collisions. This problem is framed as a problem of optimal alarm choice given predictive models for vehicle location and motion. Techniques for real-time collision detection are surveyed and grouped into three classes: random Monte Carlo sampling, faster deterministic approximations, and machine learning models trained by simulation. Theoretical guarantees on the performance of these collision detection techniques are provided where possible, and empirical analysis is provided for two example scenarios. Results validate Monte Carlo sampling as a robust solution despite its simplicity

Crosstalk and the Dynamical Modularity of Feed-Forward Loops in Transcriptional Regulatory Networks

Network motifs, such as the feed-forward loop (FFL), introduce a range of complex behaviors to transcriptional regulatory networks, yet such properties are typically determined from their isolated study. We characterize the effects of crosstalk on FFL dynamics by modeling the cross regulation between two different FFLs and evaluate the extent to which these patterns occur in vivo. Analytical modeling suggests that crosstalk should overwhelmingly affect individual protein-expression dynamics. Counter to this expectation we find that entire FFLs are more likely than expected to resist the effects of crosstalk (approximate to 20% for one crosstalk interaction) and remain dynamically modular. The likelihood that cross-linked FFLs are dynamically correlated increases monotonically with additional crosstalk, but is independent of the specific regulation type or connectivity of the interactions. Just one additional regulatory interaction is sufficient to drive the FFL dynamics to a statistically different state. Despite the potential for modularity between sparsely connected network motifs, Escherichia coli (E. coli) appears to favor crosstalk wherein at least one of the cross-linked FFLs remains modular. A gene ontology analysis reveals that stress response processes are significantly overrepresented in the cross-linked motifs found within E. coli. Although the daunting complexity of biological networks affects the dynamical properties of individual network motifs, some resist and remain modular, seemingly insulated from extrinsic perturbations-an intriguing possibility for nature to consistently and reliably provide certain network functionalities wherever the need arise

Observation of a two-dimensional spin-lattice in non-magnetic semiconductor heterostructures

Tunable magnetic interactions in high-mobility nonmagnetic semiconductor heterostructures are centrally important to spin-based quantum technologies. Conventionally, this requires incorporation of "magnetic impurities" within the two-dimensional (2D) electron layer of the heterostructures, which is achieved either by doping with ferromagnetic atoms, or by electrostatically printing artificial atoms or quantum dots. Here we report experimental evidence of a third, and intrinsic, source of localized spins in high-mobility GaAs/AlGaAs heterostructures, which are clearly observed in the limit of large setback distance (=80 nm) in modulation doping. Local nonequilibrium transport spectroscopy in these systems reveals existence of multiple spins, which are located in a quasi-regular manner in the 2D Fermi sea, and mutually interact at temperatures below 100 milliKelvin via the Ruderman-Kittel-Kasuya-Yosida (RKKY) indirect exchange. The presence of such a spin-array, whose microscopic origin appears to be disorder-bound, simulates a 2D lattice-Kondo system with gate-tunable energy scales.Comment: 7 pages + 4 figs. To appear in Nature Physics. This is the original submitted version. Final version will be posted six months after publication. The Supplementary Information can be downloaded from: http://www.physics.iisc.ernet.in/~arindam/Supplementary_Information_NPHYS-2006-08-0 0812B.pd

Quantisation of Hopping Magnetoresistance Prefactor in Strongly Correlated Two-Dimensional Electron Systems

We report an universal behaviour of hopping transport in strongly interacting mesoscopic two-dimensional electron systems (2DES). In a certain window of background disorder, the resistivity at low perpendicular magnetic fields follows the expected relation $\rho(B_\perp) = \rho_{\rm{B}}\exp(\alpha B_\perp^2)$. The prefactor $\rho_{\rm{B}}$ decreases exponentially with increasing electron density but saturates to a finite value at higher densities. Strikingly, this value is found to be universal when expressed in terms of absolute resistance and and shows quantisation at $R_{\rm{B}}\approx h/e^2$ and $R_{\rm{B}}\approx 1/2$ $h/e^2$. We suggest a strongly correlated electronic phase as a possible explanation.Comment: 5 pages, 3 figures, Proceedings of EP2DS 17, Reference adde

A predictive computational model reveals that GIV/girdin serves as a tunable valve for EGFR-stimulated cyclic AMP signals.

Cellular levels of the versatile second messenger cyclic (c)AMP are regulated by the antagonistic actions of the canonical G protein → adenylyl cyclase pathway that is initiated by G-protein-coupled receptors (GPCRs) and attenuated by phosphodiesterases (PDEs). Dysregulated cAMP signaling drives many diseases; for example, its low levels facilitate numerous sinister properties of cancer cells. Recently, an alternative paradigm for cAMP signaling has emerged in which growth factor-receptor tyrosine kinases (RTKs; e.g., EGFR) access and modulate G proteins via a cytosolic guanine-nucleotide exchange modulator (GEM), GIV/girdin; dysregulation of this pathway is frequently encountered in cancers. In this study, we present a network-based compartmental model for the paradigm of GEM-facilitated cross-talk between RTKs and G proteins and how that impacts cellular cAMP. Our model predicts that cross-talk between GIV, Gαs, and Gαi proteins dampens ligand-stimulated cAMP dynamics. This prediction was experimentally verified by measuring cAMP levels in cells under different conditions. We further predict that the direct proportionality of cAMP concentration as a function of receptor number and the inverse proportionality of cAMP concentration as a function of PDE concentration are both altered by GIV levels. Taking these results together, our model reveals that GIV acts as a tunable control valve that regulates cAMP flux after growth factor stimulation. For a given stimulus, when GIV levels are high, cAMP levels are low, and vice versa. In doing so, GIV modulates cAMP via mechanisms distinct from the two most often targeted classes of cAMP modulators, GPCRs and PDEs